The Beginner's Computer Science Roadmap to 6-figure career

1. Introduction: You ready to break into tech, but don't know where to start? Whether you are a complete beginner or switching careers, this course is your step by step guide to building real skills and launching your journey in computer science. Hey, I am Zenith, an experienced data scientist and educator with over seven years of teaching expertise in computer science and data science. So start with the fundamentals of computer science with the core ideas that power the modern technology. Then we will dive into Python programming where you will learn how to write ten code control flows and how object oriented programming works. Next, we'll master data structure and algorithm, learning how to solve real world problems using searching, sorting, and recursion. You will explore the software development life cycle, debugging techniques and testing strategies that real engineers use every day. Then we'll dig into databases, both EQUAL and no SQuL so that you can confidently bag query and manage data. Plus, you're going to develop your full stack development skill set, learning what goes behind building interactive websites and dynamic server side applications at a fundamental level. Do you want to explore the cutting edge technology? We are going to introduce you to the basics of machine learning and artificial intelligence? Of course, we are going to cover cybersecurity fundamentals so that you understand how to protect the system and data. If you're ready to learn by doing, gain in demand skills and build a solid foundation for a career in tech, this course is for you, so let's get started. 2. Introduction to Computer Science: In the world where technology drives nearly every aspect of our lives, one field that stands out at the core of it all is computer science. But why exactly computer science, and why is it so important high IN Zen and welcome to our first lesson in exploring computer science. Today, we will answer questions like what is computer science? We will dive into its broad scope and see how it shapes everything from our apps, on our phones to AI systems that are changing industries. At its core, computer science is a study of computers and computational systems. It's more than just coding. It's about understanding how information is processed, stored, and transmitted. It involves designing systems, solving complex problems, and innovating new ways to interact with the digital world. Computer science can be broken down into several key areas. Algorithms are the heart of computer science. These are step by step procedures or formulas used to solve problems efficiently, whether it's sorting a list or navigating through robots, through a maze. Algorithms are everywhere. Programming and software are the most well known aspects of this subject. It's about writing core to create software, whether it is for a mobile app, a website, or an operating system. Data structures. How we store and organize the data is collusion. Data structures like arrays, list entries allow us to manage large amounts of information in efficient ways. The upcoming industries, artificial intelligence and machine learning are allowing computers to learn from data, make decisions, and improve over time without any human interference. Then come cybersecurity, with the increasing amount of sensitive data stored digitally, cybersecurity ensures that the information remains safe from hackers and spo threats. Computer science is not just confined to Silicon Valley or tech world. Its application touches almost every industry and sector that you can imagine. Let us take a look over here how computer science is transforming various fields. In healthcare, computer science is changing the way we diagnose diseases and manage patient care from advanced imaging technologies to AI algorithms that help assist in early diagnosis. It enables better treatment options and improved patient outcomes. Coming to finances in finances, a subject is used in areas like high frequency trading, fraud detection, personal finance, apps, et cetera. Algorithms analyze the market trends and optimize investment while cybersecurity protects your financial data from threats. Coming to the entertainment industry from video games to movie CGIs, animations and virtual reality, even your personalized content recommendation systems all rely on the principles of computer sites. In transportation, computer science plays a vital role in the development of autonomous vehicles, flight navigation systems, and even traffic management. Making travel safer, faster and more efficient. In the education industry, it helps in e learning platforms, online classrooms and interactive learning tools that make education more accessible and much more engaging. The AI based tutoring system also helps in personalizing the learning experiences. Retail and e commerce. In retail, computer science powers recommendation mines, personalized shopping experience, and efficient supply chain management. It's at the core of online shopping platforms and customer data analytics. As you can see, computer science is a vast and dynamic field from creating software to designing complex algorithms, from improving healthcare to transforming entertainment, the impact of computer science is all around us. And the future of computer science, if you ask, it's even more exciting. We are entering the age of quantum computing, self learning, AI, and advanced robotics, which will continue to shape the way we live and work. So now that you have a broad understanding of what computer science is and how it impacts so many aspects of our world, in the next lesson, we will dive deeper into the history of computing and explore the pioneers who laid the groundwork for the technologies that we use today. Let's continue this journey into the fascinating world of computer science. I will see you in the next lesson. 3. The History and Evolution of Computing: Up to today's lesson, the history and evolution of computing. In today's lesson, we will dive into the fascinating journey of how computing started, the major milestones along the way and how programming languages evolved to become what we use today, whether you are a beginner or someone with experience, there's always something new to discover about this incredible fee. Let's begin at the very beginning. The history of computing goes way back. And it's not just about the technology we know today, but how early ideas and innovations laid the foundation for everything that followed. So where did it all begin? In the early 19th century, a man named Charles Babbage envisioned the first mechanical computer called the Analytical engine. Though it was never completed, this device was revolutionary. It was designed to be programmable, meaning that it could perform any mathematical calculation, Babbage's idea would eventually inspire future generations of engineers and mathematicians. Along with Babbage, we have Ada Laplace. Often considered the world's first computer programmer, she saw the potential of analytical engine and wrote the first algorithm intended for execution on a machine. Her work laid the groundwork for modern computing and concepts. Fast forward to the 1940s when we saw the first truly electronic computer, one of the most famous of these words, ENI, AC electronic numerical integrator and computer. EL during World War two, would could perform complex calculations thousands of times faster than any human, which was a major breakthrough for science and engineering. Important development around the same time was creation of Turing machine conceptualized by Alan Turing, Turing's work established the theoretical foundation of computation and computability. Even today, the concept of turing machine helps define the limits of what can be computed. By the 1950s and 1960s, we moved from huge room size machine to more compact systems. Mainframe computers became most common in business and universities. These machines were the backbone of early data processing and scientific research. The real shift towards personal computing actually came in the 1970s, which was the invention of microprocessor, which made it possible to shrink computers down to the size that could sit on a desk. This led to creation of personal computers like Apple one and the IBMPC which were revisar in which making computer accessible through individual, two of the most iconic figures in the era was Thobs and Bill Cates who helped define the personal computer computer revolution. Jobs with Apple focused on creating user friendly designs, while Gates and Microsoft were instrumental in developing the software that made computers useful for millions of people coming to part two, which is development of programming languages. Now let's talk about the development of programming languages because a computer is only as powerful as the program that it runs on. Early programming languages such as assembly, portal, and lips. In the earliest days, computers were programmed using machine code, which is a series of zeros and ones. As you can imagine, this was incredibly tedious and error prone. To make programming easier, early programming languages developed assembly languages which are a step above the machine code, but it is still very low level. In the late 1950s, we saw a creation of the first high level programming language, which was poten, short for Formula Translation, which was developed to help scientists and engineers perform complex calculations. Around the same time, list or language developed for artificial intelligence research also emerged. By 1960s, language called Basic Bgners All purpose symbolic Instruction code was created to make programming more accessible. It was designed to be easy for students and beginners to and it played a huge role in bringing programming into schools and universities. In 1970s, we saw introduction of C, one of the most influential programming languages ever. C allowed programmers to write more efficient portable code, and it became the foundation of modern programming languages that include C plus plus, Python and even more modern web technology. As computing advanced, so did the languages in the 1990s and 2000. We saw languages like JavaScript, Java and Python gain more popularity for building web applications, software, and even handling artificial intelligence tasks. Each language was designed to solve specific problem and making programming more efficient in different areas. So to sum it up, the evolution of programming languages went from machine code to assembly, then to early high level language like Forten list and basic, and finally to more advanced and specialized languages like C, Python, and Javascript. From those first mechanical devices to today's powerful computers and sophisticated programming languages, programming has come a long way, and we are still evolving. The development of artificial intelligence, quantum computing, and other emerging technologies suggests that the history of computing is far from over. Thanks for joining today and in the next lesson, we will start with some basic components of computers. 4. Basic Computer Components: Welcome to this course on basic computer Cfonens. In this module, we will cover the four fundamental elements that make up a computer, the CPU, the RAM, storage devices, and input output devices. The CPU central processing unit. The CPU is often referred to as the brain of the computer. It processes instructions and manages tasks. It performs all the calculations, data processing, and controls the flow of information into the computer. Functions of CPU includes fetching the instruction from the memory, decoding them into a format that machine can understand execution of the instruction to perform actions. CPU consists of three main paths, ALU, that is arithmatic logic unit that performs all arithmatic and logical operations, the control unit that directs the operations of the processor, the register, that is a small and high speed storage location that holds data and instructions temporarily. RAM, RAM is random access memory. RAM is the temporary memory that the computer uses to store data that is actively being used or processed. When you open a program, it is loaded from the storage into RAM for faster access. Why is RAM so important? Because it is fast, but it is volatile, meaning that it loses all its data when the power of your computer is turned off. The more RAM a computer has, the more tasks and program it can handle simultaneously. There are two major types of RAM DRAM and SRAM. DRAM is dynamic RAM, slower, but cheaper and commonly used in most systems. SRAM, that is static gram is faster but more expensive, typically used in cache memory, coming to storage devices. Storage devices are where all your data and files are kept for long term. Unlike RAM, storage is non volatile, meaning it retains information even when the computer is powered off. Common types of storage include HDD, hard disk drive, a mechanical device that uses spinning disks to read and write data. SSD Solid State Drive, a faster, more reliable device that stores data in flash memory without moving parts. The third is cloud storage. Data stored on remote servers accessed over the Internet, offering flexibility and backup. Next, we move to input and output devices. Input and output devices allow you to interact with your computer. Input devices such as keyboard allows you to type text and commands into the computer. The mouse pointing device used to interact with a computer's graphical interface at a microphone like the one that I'm speaking to you through captures audio as an input. The output devices, like monitor displays text, images and videos to the user printer that converts digital documents into physical copies, rekersOput sounds for audio based content. Now we are going to look at how computers work. Here we are going to look at fundamental concepts of how computers process data. We are going to learn about winery. We are going to learn about machine code and how the CPO processes information. First, let us look at binary, the language of computers. At the most basic level, computers operate using the binary system. This system uses only two digits zero and one. These are called bit binary digits. How does this work? Everything in a computer is ultimately represented as binary code, whether it's numbers, letters or images. A single bit is the smallest unit of data, and a group of eight bits make a byte. Computers uses binary because electrical circuits can easily represent two states. On that is one and off, that is zero. Now we look at machine code, the CPU's native language. Machine code is a low level programming language that the CPU understands directly. It's made up of binary instructions that the CPU can execute. How does a machine code work? Each instruction in the machine code corresponds to a specific action like adding numbers or moving data. Then machine code instructions are stored in memory and fetched by the CPU during the execution time. Although machine code is efficient, it is hard for humans to read, which is why higher level programming languages like Python or Java are used to write softwares, which is then translated into machine code. EPUs role in processing data. The CPU is responsible for executing the instructions in machine code. Here's how it is done. First of all, fetch. The CPU retrieves an instruction from the memory. Decode. The instruction is decoded into an action, example, add, subtract, et cetera. Then the third step is to execute the CPU performs the action and stores the result back in the memory or sends it to an output device. This cycle happens billions of times per second in a modern processor. Now that we have come to an end, in this course, we have covered the fundamental components of computer like CPU RAM, storage, and input output devices. We also explore how computers process information using binary, machine code, and the CPU. Understanding these basic concepts is the key to grasping how computer works and how components interact to perform tasks. Now that you have a foundational knowledge of computer hardware, you can explore more advanced topics. Thank you for joining me in this course, and I will see you in the next lesson. 5. Introduction to Algorithms: Into the course on introduction to algorithms. In this module, we will dive into what is an algorithm, explore some common examples and discuss the importance of algorithmic thinking in problem solving. What is an algorithm? An algorithm is a step by step procedure or a set of rules to follow to solve a problem or accomplish a task. Think of it like a recipe book. It outlines the exact steps that are needed to achieve the desired result. The key characteristics of an algorithm is its finiteness. An algorithm must terminate after a finite number of steps. Is definiteness. Each step of the algorithm must be clearly and unambiguously defined. The input, the algorithm must accept input, which is the data required to execute the process. The output, the algorithm must produce outputs, which is the result of the computational task. Effectiveness, the step must be simple enough to be carried out ideally by a machine. In simple terms, algorithms guide you from the starting point, which is the input to the desired outcome, that is the output in a well defined way. Common examples of algorithms. Algorithms are used everywhere from daily tasks to complex computational problems. Let us look at some common examples. First one would be recipe for cooking. Input here would be the ingredients. The steps would include chopping up vegetables, cook in oil, add spices, stir occasionally. The output would be a finished dish. Another example could be sorting a list of numbers. One of the most common algorithmic problems is sorting. A simple algorithm bubble sort arranges the number in increasing order by repeatedly swapping adjacent elements if they are in wrong order. Searching for an item in a list. Example, linear search. Input here would be a list of items and a target item to search. The steps could be going through each and every item in the list and check if it matches the target. Output would be the index or the position of the target item or it would be northbound. Another example could be GPS navigation. Here, the input would be start location and the destination we want to go to, the steps would include the algorithm that calculates the best route by considering distance, time, and the traffic conditions. The output here would be direction for the fastest and the shortest route. The importance of algorithmic thinking, algorithmic thinking is the ability to break down a problem into smaller manageable steps and find the efficient solution. It is an essential skill for programming, problem solving, and even decision making in daily life. Here is why algorithmic thinking is so important. First of all, efficiency. A well designed algorithm can solve a problem much faster with fewer resources. For example, searching for an item in a sorted list is much faster with a binary search algorithm compared to a linear search algorithm. Problem solving, algorithms help us break down complex problems into simpler tasks. Instead of solving everything at once, we can approach a problem step by step. Optimizing resources. Algorithm allows us to optimize time, memory, and processing power. In computing, this is often a means to choose algorithm that runs faster and uses less cavery. Real world applications from self driving cars to social media feeds, algorithms are used to make critical decisions in real time. Understanding how they work helps us to use technology more efficiently. So coming to the end of our lesson in this module, we have learned algorithmic thinking helps us break down complex problems, find efficient solutions, and improve the way that we use technology in our day to day life. As you continue to learn more about algorithms, which we are going to dive deeper in the coming up lessons where an entire module is dedicated to algorithms, we will continue our journey over there. We will discover how this powerful tool can solve wide range of problems across various fields. Thank you for joining me in this course. Keep exploring the world of algorithms, happy learning, and I will see you in the next module. 6. Introduction to Programming: Hello, and welcome to the introduction to programming course. Today, we are going to explore some fundamental concepts that are essential to getting started with programming. First, let us dive into the question, what is programming? What is programming? Programming is a process of designing and creating a set of instructions that tell a computer how to perform a task. These instructions are written in a programming language such as Python, Java, C plus plus, and many more. Think of programming as giving instructions to the computer a recipe to follow. You, as the programmer, are the chef, and the computer is the kitchen. Your instructions tell the computer exactly how to prepare the dish step by step key aspects of programming. First one is problem solving. At its core, programming is about solving problems. When you start to write a program, you are creating a solution to a problem. Next is algorithm. The series of steps and instructions that solve problem is called an algorithm. A program is essentially an algorithm written in specific programming language. Logical thinking, writing effective programs require logical thinking. You must break down the problem into smaller and manageable pieces. Some examples of programming would be web development. Programming is used to build websites and web applications. For example, HTML and JavaScript are used to design the structure and interactivity of web pages. Mobile programming allows us to create apps for smartphones such as social media, apps, games, and messaging apps. Artificial intelligence, AI and machine learning systems are also built using programming languages, helping computers learn from data. Now that we understand what programming is, we are going to discuss how programs are executed by the computers. Specifically, we'll talk about compilers and interpreters. Two key tools that help translate your program into a language that the computer can understand. Let's start with compilers. A compiler is a program that translates the entire source code of a program into machine code that is binary that the computer can execute. This translation happens all at once, and after that, the machine code is executed. Think of compiler as a translator who translate a whole book of one language to another before the book is read out loud. Advantages of a compiler, speed. Since the source code is translated before execution, the program can run faster once compiled. Optimization Compiler often optimize the code, making it run more efficiently. Now let's move on to interpreters. A interpreter, on the other hand, translates a programmer's source code line by line into the machine code. Instead of converting the entire program in one go, it processes the code, one instruction at a time. Think of interpreter like a translator who reads and translates the book one sentence at a time, as it is being read out loud, advantages of the interpreter. Easy Tbugging. Since the interpreter runs line by line, you can quickly see which part of the program is causing errors. Immediate execution, the program can run immediately without the need for a separate compilation step. E differences between compiler and interpreter, the compilation. A compiler translates the entire program at once while the interpreter translates the program line by line. Execution time. Compiled programs are generally faster during execution whereas interpreted programs are slower due to the real time translation. Error handling compilers show all the errors after compilation while interpreters stop at the first error they encounter and show it immediately. To summarize, programming is an act of writing instructions for a computer to follow. Compilers translate the entire program into machine code before execution while interpreters translate and execute the program line by line. Understanding how compilers and interpreters work is crucial for choosing the right tool and languages for your projects. In the next module, we will dive deeper into the basic concepts of programming such as variables, data types, and control structures. We'll also explore some programming languages and how to get started writing your first program. Thank you for joining this session, and I will see you in the next one. 7. Python Programming Basics: On to Python programming basics. In this module, we are going to cover the most important concepts in Python. Python is a high level interpreted programming language known for its simplicity. Is sittax is designed to be intuitive, making it an excellent choice for Bigas. Python is used for web development, data science, automation, artificial intelligence, and much more. Syntax of programming language refer to the set of rules that defines the combinations of symbols that are considered to be correctly written in order to create a structured program. In Python, the syntax is very simple and readable. To show you the pieces of code, I'm going to use Google Collab notebook. You can do the same to run these programs. I will also be providing the Python files in the resource section so that you can execute these programs on your own. Let us start with the Python syntax. Here, as you can see, print is a inbuilt function or also known as building function in Python. The Hello world that is written in double inverted commas is a string, which we want to print on the screen. To run this piece of code, I will be using the Run sell button, or you can also press Control plus Enter. The built in print function prints out anything that is written within the round parenthesis. As over here we have written Hello world. It has exactly printed the same thing on our result screen. It is not going to check if the syntax of it is right or wrong. The prill function prints out anything that is written in it as it is. The first key syntax rule in Python is indentation. Python uses indentation, which is actually four spaces to define code blocks. Python is case sensitive, so anything that is written in uppercase or lowercase are both different there is no need of semi coolts. Unlike languages like C or Java, Python doesn't require semicolons to end the statement. Comments start with a hash symbol. They are ignored by the Python interpreter, but can be useful to explain pieces of code. When we execute this block of code, only the print built in function is going to get executed. The Python interpreter is going to ignore this comment over here because it has started with a hash sign. Variables in Python. What is a variable? A variable is a container for storing data values in Python. In Python, you do not need explicitly declare the type of variable. Python automatically fingers out what kind of variable you are looking for. As you can see here, X is a variable that stores the value of five and name is a variable here that stores the string Alice, naming the variables. When naming the variables in Python, there are a few important rules that we must follow. First one, the variable names can include letters, numbers, and underscore. The variable names cannot start with a number. There should be no spaces in the variable name. To indicate spaces, we can write underscore. Variable names are always case sensitive. As you can see here, these are some valid variable names. Age is equal to 25. First name. You can see here, I wanted to write first name. Because I wanted to indicate a space, I have used an underscore sign. First underscore name is John and user ID again, to separate user from ID, we have used an underscore sign. So user ID is equal to one, two, four. These are some valid variable names. There are some invalid variable names like you can see over here. The first one is second underscore name. Now because there is two at the starting, which is a number, this is not a valid variable name. The next one is my name is equal to Sara, but there is a space between M and name. So this is an invalid variable name. Next here, we see data types in Python. In Python, there are several built in data types that are used to define the type of data a variable can hold. Let's explore some of the common data types. The first one is integer. It takes in whole numbers which are positive or negative. Example, age is equal to 25. Then we have flow which takes in decimal numbers. Example Pi, which is Pi is equal to 3.14. Then we have string, a sequence of characters that is text, example greetings is equal to hello. Then we have Boolean, which represents either true or false. Example is active, equal to true. Then we have list, a collection of items which can be of different data types. Example fruits is equal to apple, banana, cherry. Then we have dictionary, a collection of key value pairs, example person, name, name is Alice, age is 25. Here, name is the key and Alice is the value, and age is the key, and 25 is the value. Checking data types, you can use the built in type function to check the data types of a variable. Here X is equal to five. I have written a command print type X, which is going to check the data type of X. If we execute the program, you can see it is class INT. It represents integer. It means that X is an integer. Next, we have operators in Python. Operator is a symbol that performs an operation on one or more values, which is also known as operants. Python has several types of operators. Let us explore some of them. Matic operators that are used for mathematical operations. Some of them are addition, subtraction, multiplication, division, flow division, modulus, and exponents. Let us look at an example. Here we are taking two variables X and Y. Both of them, as you can see, are integers. X is equal to ten, Y is equal to three. On these two variables, we are going to perform arithmetic operations of addition, division, and modulus. Let us execute the program over here because there are three print functions over here, we are going to receive three answers. The first one X plus Y, which is ten plus three is 13. X divided by Y, 10/3 is 3.33. X modulus Y, which means the remainder of 10/3, which is one over here. Next one is comparison operators, used to compare two values, and always the output is going to be in terms of true or false. These include equal to, not equal to, greater than, less than, greater than or equal to, less than or equal to. Here is an example of comparison operators where we have two variables X and Y, X is equal to five and Y is equal to eight. Going to use comparison operators equally equal to where we compare if X is equally equal to Y, it means that both X and Y has same values or X is less than Y, which means that if the value of X is less than Y. Again, I repeat that the answer is going to be in terms of true and false. The first one has returned false because five and eight are not equal. The second one is X is less than Y, which is true. That is why we have a true out. Logical operators are used to combine conditional statements. The first one is add. If both conditions are true and is going to return and answer true. If one of the condition is false and is going to return false, then we have operator. Or at least one of the conditions should be true to get a true result. If both are false, then we are going to get a false result. Then we have the not operator which is just going to invert the result. If the result is true, then we are going to get a false. If we have false, then you are going to get a true. As you can see, we have two variables. X is equal to ten and Y is equal to 20. The first print statement consists of two conditions. The first condition says X is equally equal to Y. That means that X and Y has the same value. And the other condition states that Y is greater than X, which is true in the case, as you see here, Y is equal to 20, which means that Y is greater than ten. In the first statement, you can see that the first condition is false and the second condition is true. But when we execute, we are going to see that this is going to give us a false answer. As I said, and both the conditions should be satisfied to give a true answer. The next statement you can see over here, the first condition says that X is less than Y, which means that ten is less than 20, which is true. We are going to use the operator and the second condition is Y is equally equal to X. You can see that the first condition is true and the second condition is false. But because we are using an operator, we are still going to get a true result. Why? Because you know that gives us an positive result or a true result, even if one of the condition is true. Let us execute this. You can see the first statement we get false and the other one we get true. Here we are going to use then operator, not Z is equal to two, but because we are doing not Z, we are going to get a false. Next, we move to expression in Python. Expression is any combination of values, variables, operators and functions that the Python interpreter can evaluate to produce results. Here we have X is equal to five and Y is equal to three. The expression that we have written is X plus Y raised to the power of two in the results variable. Print out this result variable, we are going to use the print function and because this expression uses the operator precedence rule, Y raised to the power two is going to be executed first and then the result is going to be added to X. If we run this, we are going to get the result. Next, we have order of operations. Ethan follows operative precedence rule, which determines the order in which operations are going to be evaluated. Here, the general order from highest to lowest precedence would be first parenthesis, then exponentials, then multiplication, division, floor division, and modulus, then addition and substraction. In this order of operation example, we have written results is equal to three plus two roots to the bower of four. Ratio the power of four is going to get calculated first and then it is going to be added to three. If we run this program, we get an answer of 90. To summarize, Python syntax is simple and readable using inentation instead of braces for a code block. Variables are containers for storing data and Python dynamic A determines their data types. Data types in Python include integers, slots, string, boolean, lists and dictionaries. Operators allow us to perform arithmatic comparison and logical operations. Expressions are combination of variables and values and operators that result in a IA. In the next module, we are going to explore control flow in Python, including how to use conditional statements like INL and loops like four and il loop. You will also learn how to write functions to organize your code. Thank you for attending this session, and I look forward to seeing you in the next 8. Control Flow in Python: Come to the next module, control flow in Python. In this module, we will explore the fundamental control flow structures in Python, if statements loops and functions. Let us start with the first and the most essential concept, I L I Panthn if statements allow you to make decisions in your code. Based on the certain conditions, you can execute different blocks of code. The basic syntax for n if statements looks like this. First, we write the keyword if and then corresponding to that, we write the condition that we to be ten inside that we are going to write the block of code, the if condition must fulfill. If the condition is not fulfilled, the program's control automatically goes to the s block. For that, we write the s keyword. Inside the s keyword, we write the block of code that would be executed if the condition is false. Here, you can see that in this example, we have g is equal to 18. If the age is greater than or equal to 18, the program will print you are an adult. If the age is less than 18, the program will print you are a minor. Let us execute this program. You will see that we have written age is equal to 18. If I execute this program because it fulfills the condition, we are going to get a result. You are an adult. If I change the value over here and make it 13, g is equal to 13. If I execute this program because my condition is not fulfilled, I am going to get the second output, which is a minor, L if for multiple conditions. Sometimes you need to check more than two conditions. This is where if short for sf comes in. You can change multiple conditions using I L s. In this piece of code, you can see we have written score is equal to 85. We have written the first condition in an if statement. I score is greater than or 90, the person is going to get a grade A. Else, if the score is greater than or equal to 80, the student is going to get a grade P. Else if the score is greater than or equal to 70, the person is going to get a grade C. Else, the last S statement over here says that if none of this is true, the person is going to get a grade F. Let us execute this program over here. Because my score is 85, it falls into grade B loops in Python. Now let us move on to loops. Loops are used to repeat a block of code multiple times. There are two main types of loops. We have the four loop and the y loop. Let us first take a look at four fall loop is used when you want to iterate over a sequence, such as list, a string, or a range of numbers. The basic syntax of a fall loop looks something like this. It should include the two keywords for and in. Between them, we must write the item that we want to iterate over and then we have to write the sequence that it is supposed to follow. Inside this fall loop, we are going to write our block of code. Here I have a list of fruits that is apple banana cherry. Then I go to itrate over these items one by one. For that, I will use a fool loop. I will write a keyword for followed by the items name that is fruit over here, and this fruit is inside the fruits list that we have already declared. Inside the block of code, I'm just going to print out fruit one by one. When I run the folloop it is going to itrate over each item and it is going to print out apple, banana, cherry. In the first iteration, the program has printed out apple. In the second iteration, it has printed out banana. In the third iteration, it has printed out cherry. After cherry, there is no item present inside the list, so it does not print out anything and the for loop breaks. The range function, Python provides us with range function which generates a sequence of numbers. This is especially useful for iterating over a specific range of values. Here the fall loop is going to iterate over the item I, which is present inside the range function five. Because it is written as range five, it means that the first iteration is going to result as zero, then one, then two, then three, then four. It is not going to iterate over five. If I run this program, I'm going to get zero, one, two, three, four. The next one is y loop. The y loop is used when you want to repeat a block of code as long as a specific condition is true. The syntax for this is we write the keyword Y followed by the condition that we want to be fulfilled. If the condition is true, the block of code is going to get executed. In this example at the beginning of the program, count has been set to zero. When we run of Y loop, the condition over here is that the count must be less than five. We must print the count as it is over here and then we are going to increment it by one number. In the first iteration, count is equal to zero. Because zero is less than five, it is going to print it as it is, and then it is going to give count plus one. If I run this program, you will see that this program will be run. When we execute this program, it is going to go till four because as soon as count reaches four, count less than five is going to be true. It is going to print out the count which is four currently, and then it is going to I count plus one. So the new count is going to become as five. The new count will not be able to fulfill the condition. That is why the program terminates. It is important to note that the Y loop can potentially lead to infinite loops if the condition never becomes false. Always ensure that the condition eventually changes to avoid this. Y is equal to true print, this will run forever. If I execute this program, because the condition itself is true, this program is going to run infinitely and see that the execution does not stop, so I will have to stop it my prevent infinite loops, always ensure that you have an exit condition or a way to break out of the loop. For such conditions, we can use the break statement. Now let's move on to functions. Functions allow you to group a block of code that performs a specific task. Once defined, you can call a function whenever needed, which makes your code more modular and reusable. A function in Python is defined using the DEF keyword, which is def keyword. The basic syntax of a function looks something like this. We are going to write the keyword DEF, and then it is followed by the function name followed by round parenthesis, and inside that, we are going to write the code block. Here we have defined a function called GET, which prints, if we run this, if we have defined the function correctly, it is going to get executed. Have not received any output on the screen because we have not called the function. To call the function, you're just going to write the function name. If I run this part of the program, you will see that the print hello world is going to get executed because we have called the function functions with parameters. You can also pass parameters to functions to make them more flexible. Parameters allow us to pass data into the function for further processing. For example, here we have made the greet function that accepts a name parameter and prints personalized greeting messages. Over here, as soon as I call Greet Ale, we are going to get Hello Alice. Because we have called Greet Bob, it is going to print out Hello Bob functions with return values. Functions can also return values. The return keyword is used to send back a result from the function. In this piece of code, we have the add function that takes in two parameters A and B, and it adds them together. B we have added the return keyword over here, the result after calling the function is going to be stored in this particular variable called as result, and then later on, we can print it out. If I run this program over here, we are going to get the result of the addition of five and three. Next, we have user defined functions with multiple parameters. In Piton, you can define a function that accepts multiple parameters to perform more complex tasks. The function can also have default values for parameters, which will be used if no argument is provided during the function call. In this program, here, the message parameter has a default value of hello. If no value is passed during the function call, it will use the default. Let us run the program because in the first GET function, we have just passed the name which is Alice and not passed the message, it is going to print out Hello Alice. But in the second GET function call, we have passed on the name as Bob and the message as goodbye, so it has printed goodbye bob scope of a variable in functions. Python, variables are defined inside a function are local to that function and can't be accessed outside of it. Variables defined outside of any function are global and can be accessed throughout the program. In this program over here, X is equal to ten is a global variable. X is equal to ten can be used throughout the program. Test function X is equal to five is a local variable and can be used only inside the function. Let us run the program, and you will see that when I call the test fum shell because it has a local variable inside eight which is declared X is equal to five, it prints out print X as five. But when I just write print X over here, it takes in the global variable declaration, which is X is equal to summarize ifs statements are basically used to make decision based on conditions allowing different blocks of code to run depending on the results of the condition. Loops are used to repeat a block of code. The fall loop is used to iterate over sequences, while the Y loop is used when the condition needs to be checked before each iteration. Functions are used to group a block of reusable code. Functions can accept parameters, return values and can be defined to perform a specific task. User defined functions, these functions can take up multiple parameters and return values, making them powerful tool for modular program. Thank you for joining 9. Data Structures in Python: Come to the data structures in Pikin course. In this module, we'll explore four important data structures in FI. These structures are essential for organizing and manipulating data effective. First, we are going to look at lists in Python. A list is an ordered collection of items, which can be of different data types. Data type different data types here means that in a particular list, we can have integer, string, as well as float. Ists are one of the most commonly used data structures at Belton. Lists are mutable, meaning that their content can be changed after the creation of a particular list. Lists are defined using square brackets. The square brackets are important because we will see now that in different data structures to distinguish between them, different kinds of parentheses or brackets are being used. For lists, we use square brackets. The syntax of a list, you can see over here, first we define a variable. My variables name over here is my list. Then I define it using square brackets. The two yellow square brackets weired indicate that I have made a list. As you can see over here, I have added multiple different data types. We have one, two, three, which is an integer, we have apple, which is a string, and then we have two. It means that it is a boolean. If I run this, you see that a successful execution has been done. Is my list has been created. Then we move to accessing elements of a list. You can access individual elements in a list by their index number. The indexing in Python starts at zero. Over here, we have created a list called fruits, where I have defined it using square brackets and I have made items in it like apple, banana, and chi. Each of them has an individual index number which is exclusive to them. Apple has an index number of zero. Banana has an index number of one and cherry has index number of two. Over here, when I print out using the print function, print fruits at zero position, it means that it is going to print out the first element, which for me over here is apple. Then if I print out fruit one, it means that it needs to print out inside the fruit variable the first item. So zero and one, it is going to print out banana for again, modifying elements in a list, you can modify an element in a list by accessing its index and assigning a new value over there. For example, if I write fruits one, it means that I am pointing out to this particular element, which is banana. I want it to be changed as blueberry. I'm going to use the equal to sign over here. What this is going to do is it is going to take the blueberry string that I have written over here and it is going to place it at the first position. Now if I print my fruits list, zero position, I'm going to get apple azetis at the second position that was banana. Now I have blueberry, and then the cherry is still azetis. Irating over list items. You can iterate over the elements of a list using a four loop. One by one, we can go through the elements using four loop. For that, we are going to use the four the item name that we want to iterate over inside the list name. So over here, my lists name is fruit. I'm going to iterate over it using the print function, and I'm going to print it out one by one. So in the first iteration, I have the first element that is apple. In the second iteration, I have blueberry, and in the third iteration, we have jerry. Iteration, again, in simple words is nothing but going over one element at a time. Some more list methods are append. The append function adds an element to the end of the list. Then we have the remove function that removes the first occurrence of any element. We have the pop function that removes an item at a specific index. We have the insert function that inserts an element at the specific index. We have the sort function that sorts the list in ascending order. Ascending order is nothing but from lowest to largest, then we have the reverse, this is just going to reverse the order of the entire list. It means that the last element is going to become the first element and so on. The first one, I am writing fruitsbtEpend orange. It means that at the end of the list, I'm going to get orange. Then I am using the removed function over here, fruits dot removed Cherry. In the next step, cherry is going to get removed from our list. Then I am writing fruits dot pop zero. The element at the first index that is the zeroeth position is going to get popped. If over here, now I write fruits, again, I'm going to get the altered list. Right now I'm just left out with blueberry, orange and orange. Why orange has been repeated twice because I have run this particular code twice. If I run this program again, I'm going to get three oranges. Right now, you see that we have an error over here. This particular error has occurred because we have already removed cherry from the list. The first statement got executed, but the second one threw an error because there was no cherry in the list. You can see because the first statement over here, fruit dot append orange got executed, we have one more orange added at the end of the list. Then we have tuples in Piken. A tuple is similar to a list, but it is immutable, meaning that once created, its element cannot be changed. Tuples are often used to represent fixed collection of items. Tuples are defined using round parenthesis. The syntax for a tuple, again, we are going to write a variable name. My variable name is my underscore tuple and I'm going to define it using two round parenthesis. Again, I have used multiple data types that is integer and a strict. In this if my tuple has been created using all the correct syntax rule, we are going to get a successful execution. Next we will look at how to access tuple elements. Again, the same thing we are going to use, we are going to use the index number to access these tuples, and still the format remains the same where the first element of any data structure in computers, by default, it is starting with zero and so on, it goes. So over here at the zeroeth position or the first element is Apple. So if I run this, I'm going to get then iterating over tuples. We can iterate over a tuple in the same way that we have done with lists using four loops. Again, we are using fall loop followed by the elements name or item name that we have created in the fruits tuple. Because we are just using the print function over here, one by one, it is going to print all the elements of the tuple. Over here, we have apple. In the first iteration, we got apples, then we got banana, then we got more tuple methods include the count function and the index function. The count function returns the number of times a particular value appears inside the tuple, and then we have the index that returns the first occurrence of a particular value. There can be duplicacy in double. There can be two same strings. There can be two same integers in a tuple. So the first occurrence is going to get returned over here. Over here, I have created a tuple. My tuple is equal to one, two, two, three. You can see that two occurs twice in this particular tuple. If I print this, the count of the particular number two we are going to get, how many times does it occur? It occurs two times. And my tuple dot index three, it means that what is the position of three. Because, again, the index starts from zero, so zero, one, two, three, the position is three. We have got three. Sets in Python. A set is an unordered collection of unique elements useful when you want to store values without duplicacy, sets are mutable, but the elements inside the set must be immutable. Sets are defined using curly brackets. We have created a variable called My set and we have used curly braces over here to declare the set. The set has elements from different data types. It has integer and it has a string. If I run this, if my syntax is correct, I'm going to get a successful execution. Access the elements of set. Unlike list and apple sets do not support indexing. Since sets are actually unordered, you cannot access the elements by their position. However, you can iterate over them to look at how many elements are in your set. So for that, you are going to use Paul loop, again, writing the items name in my set and we print it out, so we are going to get the entire set with us one by one. Modify a set, we have set of functions. So we use the add function when you want to add an element to the set. Over here, when we use add function, it is not going to add the element at the end of the set. It is going to give a random position. Remove function removes an element from the set. Discard removes an element if it exists without raising any error. Pop function, it removes and returns arbitrary element from the set. In our set, we currently have elements one, two, three, and Apple. If I want to modify this set, I want to add an element called Blana and I want to remove the number three here, again, I print my set out, my we are going to get one, two Apple and Mlana. Next, we move to set operations. Sets support mathematical operations like union intersection and difference. So the union actually combines the elements of the two sets. Intersection returns the common element between two sets and difference returns the element present in one set, but it is not present in the other set. Over here, we have a set one, two, and three, and the other set is three, four and five. We are going to print out the union of both sets over here. Set A dot union set B. I want to combine these two sets. Then we have intersections, set A dot intersection set B, set A dot difference set B. For the first one, we have just merged these two sets. So we have one, two, three. You can see that we just have 13 over here because set does not allow duplicacy. We have four and five over here. Then we have intersection. The common element between two sets is three, so we have a result of three then we have difference what occurred in one set but does not occur in other set because three has occurred in set B, we just have elements one and two. A dictionary is an unordered collection of key value pairs. Each item in a dictionary has a key which is unique and a corresponding value is attached to it. Dictionaries are mutable. They are defined using CR braces and the key value pair are separated using a colon. Let us look at the syntax of a dictionary. You have declared a variable over here, corresponding to that, I have created a dictionary using curly braces and you will see that we have created we have created a key value bear. The key over here is name and the value is Alice. The key over here is age and the value is 25. The key over here is city and the value over here is New York. If I run this and all my syntax is right, this will perfectly be executed. Access the dictionary values, you can access the values associated with it, again, by using square braces, and you have to write inside the square braces, the key ran. I have created a variable called person where the key is name and age and its corresponding values are Alice and five. If I want the name Alice, I have to call it key so we are going to write print person. The person is basically the name of the variable and then using square brackets, I'm just going to pass my key over here and I'm going to get my corresponding value. To modify a particular dictionary, we use the value of an existing key and add a new key value pair to the particular dictionary. Here, a dictionary called person, and inside that I want to change the value of a 25-26. I'm just going to use the key over here to change the value. Also, I'm going to add a new key value bear called city and its corresponding value as Los Angeles. If we run this over here and we print out the dictionary again, we are going to get the new dictionary. Where we have changed the value of H and also we have added a new key value pair to iterate over dictionaries, we again use the key and the values or both in the dictionary. For the first one to iterate over keys, we can use for key in person, print all the keys. If we run this, we get all the keys for your particular dictionary. If you want all the values, you can just write this value over here is nothing but the name of the item and inside the dictionary person dot values function. This values function extracts values out of all the keys and you can print it out. So over here, you have Alice 25 and Los Angeles. If you want to print out all the key value pair, then for that, you can use the name of your variable dot items. The items function extracts all the key value pairs from any variable that has been created as a dictionary. So we have name Alice age 25 city Los Angeles. Or more dictionary methods, we have to get function that returns the value for a specific key or none in the key does not exist. Then we have the keys function that returns all the keys in the dictionary. We have the values function that returns all the values of the dictionary. We have items function that returns all the key value pairs in the dictionary. This particular dictionary, we have used the G function to get the age inside the dictionary, and we have used the key function to get all the keys in the dictionary. The first output is 25 because we are using the G function to get the value of the particular key called age in the keys function, we are using it to get all the keys in this particular so to summarize, lists are ordered, mutable collections of items. We use them for flexible data storage. Tuples are ordered immutable collections. We use them for fixed data that shouldn't be changed. Sets are unordered collection with unique items. They are great for eliminating duplicacies and perform set operations. We have dictionaries that are unordered collection of key value pairs and are ideal to be used for associating unique keys with their particular values. So thank you for joining today's session, and I look forward to seeing you in the next one. 10. Object-Oriented Programming (OOP): Welcome to the course all object oriented programming. In this module, we explore the most powerful tool of programming, which allows you to design your code around objects and classes. OOP is widely used in modern programming essential for creating scalable, maintainable, reusable software systems. What is class? In Python, a class is a blueprint of creating an object, a particular data structure. A class defines a set of attributes that is data and methods, those are functions that describe the behavior of the object. A class is like a template, which an object is an instance of that class. We have created a class called class name, and we have defined the init method and the self reference. So the init method is a special method that is used to initialize the attributes when an object is created. Self is the reference to the current instance of that particular class. Creating an object. An object is an instance of a class. You can create an object by calling the class name as if it was a function, inheritance. Inheritance is a fundamental concept of Oops that allows one class called the child class to inherit attributes and methods from another class called the parent class. Inheritance help us to avoid code duplication and also allows for hierarchical relationship between classes. The child class can reuse the methods and attributes from the parent class. It can override parent class methods if necessary, and a child class can add its own methods or attributes. Polymorphism means many shapes. In Oops, it allows objects of different classes to be treated as objects of a common base class. The most common use of polymorphism is when different classes have methods with the same name, but the methods have different behaviors based on the object. Polymorphism can also be achieved through method overloading, but Python only supports overwriting. Encapsulation is a concept of bundling the data, which are attributes and the methods, which are functions that operate on the data into a single unit that is class. Capsulation also involves restricting access to some part of the objects conference, which can prevent the accidental modification or data. In Pym encapsulation can be achieved by using public and private access modifiers. Encapsulation can also be achieved by using the prefix attributes or methods with an underscore. Single underscore for protected members and double underscore for private members. Action abstraction is a concept of hiding the complex implementation details and showing only the essential features of an object. This is achieved through abstract classes and abstract methods. An abstract class cannot be instantiated and it may contain abstract methods that must be implemented by a subclass. In Pythl, abstraction is achieved by using ABC module which stands for A, which stands for abstract base class. Summarize classes and objects are the foundation of OOP. A class is a blueprint for creating objects, and objects are instance of classes. Inheritance allows a class to inherit attributes and methods from another class promoting code, code reuse. Polymorphism enables different classes to use the same method names, but each class provides a different implementation. Encapsulation bundles data and methods together and restrict direct access to data, ensuring controlled access via methods. Abstraction hides the implementation details and exposes only essential parts of the object. Thank you for your attention, and I look forward to seeing you in the next module. 11. Introduction to Data Structures: To the introduction to data structures. In this module, we are going to answer two fundamental questions. What are data structures and why do we need them? Data structures are ways to organize and store data so that we can use it efficiently. They are essential building blocks for software development and problem solving in computer science. Think of them as containers, each designed for a specific task type. Why do we need them? Let us consider an example. Suppose you are managing a library's book catalog. You will need a way to store retrieve and update information about the books. Without an efficient system, searching for a book could take hours. This is where data structures come into play. They help us organize data logically, access data quickly, and perform operations like insertion, deletion and search efficiently. So common real world applications include social media feeds, search engines, and even navigation systems. Data structures power the system, ensuring they run smoothly and efficiently. To summarize, data structures are all about managing data effectively. Next, let us dive into the foundational data structures like arrays, linkist, stacks, and ques. Let us start with arrays. An array is a collection of elements, all of them of the same type stored in contiguous memory locations. Think of it as a row of lockers, each identified by an index. Some key features of arrays include fixed size. The size of an array is defined when it is created. Indexed based axis. You can access any element using its index, making retrieval very fast. Homogeneous data, all elements must be of the same Arrays also support various operations like traversal, accessing each element one by one, insertion and deletion, adding and removing elements. Remember that these operations can be costly since arrays have fixed size. Searching, finding an element by its value. Imagine you are developing a playlist app, and array could store the list of songs. You can quickly access the songs at a specific position or update the playlist by replacing a song. Next up, we have link lists. Unlike arrays, link lists are dynamic data structures. They consist of nodes where each node contains data and a pointer to the next node. Link lists have separate types. The first one is singly linked. Each node points to the next node. Each load holds a single value and a reference to the next node in the list. The list has a head, which is a reference to the first node in the list. We can access all items of a list using the head node. Sometimes we also separately maintain pail of a leaf list to quickly access the last for example, in Q implementation of a link list, we prefer to quickly insert at the d. The nodes are not stored in a contiguous block of memory, but instead, each node holds the address of the next node in the list. Accessing elements in a singly linked list requires traversing the list from the head to the desired node, as there is no direct access to specific node in the memed. The next one is doubly linked list. Nodes have pointers to both the next and the previous lo. A doubly linked list is a data structure that consists of a set of nodes, each of which contains a value, a two pointer, one pointing to the previous node in the list, and one point to the next node in the list. This allows for efficient traversal of the list in both directions, making it suitable for applications where frequent insertions and deletions are required. In data structure, a Du link list is represented using nodes that have three fields data, a pointer to the next node, a pointer to the previous node. The next one is a circular link list. A circular link list is a special type of link list where all the nodes are connected in form of a circle. Unlike a regular link list, which ends with one node pointing to null the last node in a circular link list points to the first node. This means that you can keep traversing the list without ever reaching na value. In circular single link list, each node has one pointer called the next pointer. The next pointer of the last node points back to the first node. And this results in forming a circle. In this type of link list, we can only move through the list in one direction. In circular DE inklist, each node has two pointers previous and next. Similar to the linklist, the previous pointer points to the previous node and the next pointer points to the next node. Here, in addition to the last node storing the address of the first node, the first node will also store the address of the last node. Link lists are flexible, but they have their trade offs. Advantage of a linklist is that it has dynamic size and it is efficient in insertion and deletion. The disadvantage of linklist is that it is slower in access time since you have to traverse node sequency and it takes up more memory due to pointer. Think of Link list as a treasure hunt. Each clue leads you to the next location, just as each node points to the next one. Now let us talk about stacks. A stack is a linear data structure that follows the last in first out principle, also known as the LIFO principle. The last item added is the first one to be removed. Stack stacks support two primary operations. The first one being push, adding an element to the top. The algorithm that the push operation follows would be that before pushing the element to the stack, we check if the stack is full or not. If the stack is full, then stack overflows and we cannot insert an element to the stack. Otherwise, we increment the value of the top by one, and the new value is inserted at the top position, the element can be pushed into the stack till we have reached the capacity of the stack. Then comes the push operation, removing the top element. The algorithm for the pop operation would be before popping the element from the stack, we check if the stack is empty. If the stack is empty, then the stack underflows. And we cannot remove any element from the stack. Otherwise, we store the value at the top, decrement the value of top by one, and return the stored top value. Other operations would include peak, which is viewing the top element and is empty, which is checking if the stack is empty or not. Stack is an incredible data structure. It is useful for scenarios like undo the functionality like in text editors, backtracking in algorithm like maze solving, function call management in programming languages. Imagine a stack of plates. You can only add or remove plates from the top. That is how stack works. Finally, let us discuss ques. A Q is a linear data structure that follows first in first out principle, also known as the FIFO principle. The first element to be added will be the first element to be removed. Picture a line in a movie ticket count. The first person in the line would get the movie ticket just like in a Q. The key operations of a Q. The first one is NQ. NQ is adding an element to the rear or the back of the u. Then we have Q. Q is removing an element from the frac. Are different types of cues. We have simple Q, which is basic FIFO behavior. Then we have the circular Q which connects the end back to the front to utilize unused spaces. Then have the priority queue in which the elements are D queued based on priority, not available time. Great jo on completing this introduction to data structures. We have covered what data structures are, why they are essentials and explore four foundational types of data structures that are arrays, linked lists, sats, and ques. So I will see you in the next lesson 12. Advanced Data Structure: Tree: Welcome to advanced data structures. In this lesson, we are going to cover fundamental data structures such as trees, binary trees, binary search trees, heap, grab, and hash tables. We will explore the structure, operations, and real life applications to help you grasp their importance in solving complex computational problems. Let us start with trees. A tree is a hierarchical data structure consisting of nodes where each node has a value and links it to its child node. The top node is called a root and the nodes without children are known as leaves. Here is an example of a tree. Let us look at its terminologies. Basic terminologies of trees include parent node. The node, which is an immediate predecessor of a node is called the parent node of that particular node. For example, here, B is the parent node of D and E. The node which is the immediate successor of a node is called the child node of that particular node. For example, D and E are child nodes of B. The topmost node of a tree or the node which does not have any parent node is known as the root node. Here E is the root node of the tree. A non empty tree must contain exactly one root node and exactly one path from the root to all other nodes. The nodes which do not have any child nodes are called leaf nodes. IJ, Kf, GH, are all leaf nodes of the tree. Children of the same parent node are known as sibling nodes. For example, D and E are called the sibling nodes. Then we have the level of the nodes, the count of the edge on the path from the root node to that particular node. Is known as the level of the node. The root node is always at zero level. Then we have the internal node. A node with at least one child is known as the internal node. Then parent or child node of that node are known as the neighbor nodes. We have any node of the tree along with its descendants is known as a sub tree. Basic operations of T data structure would include first, the creation of the tree, we create a tree in data structure, then we insert a data into a tree. Then we can search a specific data in a tree to check whether if it is present or not. Traverson of a tree are done in two ways depth for search, which is also known as DFS. For a tree, traverse all adjacent vertices one by one. When we traverse a adjacent vertex, we completely finish the traversal of all vertices which are reachable through the adjacent vertex. Or a tree, we completely traverse the left subtree and then move to the right sub tree. Breadth for search traversal is a fundamental traversal algorithm. It begins with a node, then first traverses all its adjacent once all adjacents are visited then adjacent are traversed. For example, consider organizational hierarchy in a company where the CEO is the root node, the department heads are child nodes and employees under them from subsequent levels of the tree. Then we have binary trees. A binary tree is a tree data structure where each node has at most two children referred to as the left child and the right child. This here is an example of a binary tree. The operations that can be done in binary tree, the first one is insertion. The idea is to iterate level order traversal of a given tree using a cube. If we find a node whose left child is empty, we make a new key as the left child of the node. Else, if we find a node whose right child is empty, we make a new key as the right child. We keep traversing the tree until we find a node whose either left or the right child is empty. Given a binary tree and a key, the task is to insert a key into a binary tree at the first position available in level order manner. Here we have an example where we have to input the key, which is 12th. Here, because the left child is not empty, we are going to add it to the right child of level. The next operation is deletion. The idea is to traverse the tree in level order manner. To perform the deletion in a binary tree, we must follow some steps. Starting at the root, find the deepest and most node in the binary tree and the node which we want to delete. Replace the deepest we most node data with the node that needs to be deleted, then delete the deepest write. Here we can see we have to delete the key L. As the bottom and right most node in the tree over here is 30, replace the key node N with 30 and then remove the bottom and we most node as shown over here. Next, suppose I want to delete 20. So for that, as the bottom and right most node here is 40, replace the key node that is 20 with 40 and remove the bottom and the right most node. So after division, the binary tree is going to look something like this. The next operation is the tree traversal. Tree traversal is of three kinds in order, pre order, and post order. The tree traversal techniques includes various ways to visit all nodes of a tree. Unlike the linear data structure like arrays, link list, Ques, stack, et cetera, which have only one logical way to transfer them. Trees can be transversed in different ways. The first one is in order tree traversal. The in order tree traversal is defined as a type of tree traverser technique, which follows the left root right pattern, such as the left sub tree is traversed first, the root node of that sub tree is traversed, and finally, the right sub tree is traversed. This is the binary tree that needs to be traversed. In the first step, we look for the leftmost node of the tree, which is four here and it is visited. Then the left sub tree of two is fully traversed, so two is visited next. Then the left sub tree of one is fully traversed, so one is visited. The next step three has no left sub tree, so it is visited after that. At the end, we are left with the right child of three and that at the end is visited. Next, we move to pre order traversal. In pre order traversal, it is a type of tree traversal that follows root left right policy where the root node of the subtree is visited first, the left sub tree is traversed, then at the end, the subtree is traverse. In this binary, the traversal will begin at the root of the left sub tree, which is one over here and two is visited after that. In the next step, the left child of two, that is four is visited. Then the right child of two, which is five is visited, moving to the right sub tree from the root which is three is visited over here. At the end, we are going to visit six because three has no left sub tree. So the right sub tree is visit. Next is the post order traversal. It is defined as the type of tree traversal which follows the left right root policy for each node. The left sub tree is traversed first, then the right sub tree is traversed, and finally, the root is traversed. Let us look at a binary tree over here. The first traversal is going to be at four. So the leftmost leaf node that is four is visited first. In the next step, we move from the left sub tree of two, which is five is visited. In the next step, all sub trees of two are visited, so two is visited because six has no subtree, so it is visited first. And then we move on to three, and then we move to the root node, which is one. 13. Advanced Data Structure: Binary Search Tree: Other advanced data structure is binary search tree. A binary search tree, PST is a binary tree with an ordery property. The left child of a parent node contains the value less than the parent node, and the right child contains the value that is greater to the parent's node. The operation that we can form on the binary search tree, the first of it is search. Searching here means to find or locate a specific element or a node in the data structure. In binary surg try, searching a node is easy because the elements in the BST are stored in a specific order. The steth of searching node of a binary search tree would include, first, compare the element to be searched with the root element of the tree. If the root is malted with the target element, then return the nodes location. If it does not marge, then check whether the item is less than the root element. If it is smaller, then move to the left sub tree. If it is larger, then you move to the right subtree. Repeat this procedure until the mattress form. If the element is not found or not present in the tree, then return a null. Now that we understand the searching in a binary tree, let us use an example. We can take an example of a binary search. Here, the node that needs to be found out is 20. First, we look at the root node. Root node is 45. Because 20 is less than 45, we are going to move to the left sub tree. The next node that we encounter is 15. Here, 15 is less than 20, so we are going to move to the right sub tree because over here, 20 is present in the right sub tree. After moving to the right sub tree, we found out that 20 is present, so we can return its position. Next operation is insert. A new key is always inserted at the leaf node by maintaining the property of the binary search tree. We start searching for a key from root until we hit the leaf. Once the leaf node is found, the new node is added as the child of the leaf node. Consider the following binary search tree over here. The node that needs to be inserted is 40, start at the root node. Since 100 is greater than 40, we move to the left child. Then we encounter 20, 20 is less than 40, so we move to the right child. Then we encounter 30. Again, 40 is greater than 30, so we move to the right side of 30. Because we have reached to the end of the tree, we insert 40 at the right position. The next operation is delete. Given a binary search tree, the task is to delete a node in the binary search tree, which can be broken down into three scenarios. The first case where we have to delete the leaf node in a binary search tree, which is very easy because it is at the end of the binary search tree. Here, we need to delete 20, so we look at 20 and delete it. The next case is where we need to delete a node with a single child in the binary search tree. Here we need to delete 70, we are going to replace 70 with its right chive, which is 80, and then we are going to delete 70. The third scenario is where we need to delete a node with both children in Bary surgery. Deleting a node with both children is not so simple. Here we need to delete the node in such a way that the resulting tree follows the properties of a binary search tree. The trick is to find the in order successor of the node. Then copy the contents of the in order successor the node and delete the in order successor. Here, we need to delete 50, which is the root node. So we need to replace 50 with its in order successor, which is 60. So we replace the root node with 60, and the 50 comes as a child node at the end of the binary search tree. And because it is the child node, we can easily delete. An example of binary search tree would be you can consider a phone contact list when names are arranged in alphabetical order. Searching for a contact follows a binary search tree search pattern. Narrowing it down to possibilities based on the alphabetical auto. Next, we move on to another advanced data structure called as heap. A heap is a complete binary tree data structure that satisfies the heap property. For every node, the value of its children is greater than or equal to its own value. Heap are usually used to implement protic cubes where the smallest or the largest element is always at the root of the tree. Operations on heap, we can perform insertion or insert where new element is inserted into the heap, it can bistrp the heaps vapity to restore and maintain the heap structure, a heapify operation is performed. Delete operation also known as heapify. If we delete the element from the heap, it always deletes the root element of tree and replaces it with the last element of the tree. Since we delete the root element from the heap, it will dissort the properties so we need to perform Pi operation so that it maintains the property of a heap. The HPI Pi is a process to rearrange the elements to maintain the property of heap data structure. It is done when the root is removed. We replace the root with last node and then call the HiPIPi to ensure that heap properties is maintained or heap is built. We call HIPPi from the last internal node to root to make sure that heap properties are maintained. There are two kinds. For max heap, it makes sure the maximum element is the root of that binary tree and all descendants also follow the same property. For minimum heap, it balances in such a way that the minimum element is the root and all descendants follow the same property. A real life example would be, imagine a priority queue at the airport check in counter where VIP gets priority over the regular passenger. A heap efficiency manages such priorities. 14. Advanced Data Structure: Graphs: Ex advanced data structure is graph. A graph is a set of vertices, also known as nodes connected by edges. Graph can be directed or undirected weighted or unweighted. The operations that we can perform on graphs, the first one is breadth first search, also known as BFS. BFS is a fundamental graph traversal algorithm. It begins with a node then first traverses all its adjacent once all adjacent are visited then the adjacent are traverse. BFS is different from DFS, depth first search in a way that closest vertices are visited before others. We mainly traverse vertices level by level. Popular graph algorithms would include digitras shortest path, Kf algorithm, Prim's algorithm are all based on BFF. BFS itself can be used detect graph cycle in a directed and undirected graph. Shortest path can also be found out in an unweighted graph, and many more problems can be solved with VFS. Let us take an example over here. In this graph, initially, the Q and visited array are empty. Then we visit zero and push zero into the Q and mark it with red as visited. Next, we remove zero from the front of the Q and visit the visited neighbors and push them into the queue, which is one and two. Then we remove node one from the front of the queue and visit the unvisited neighbors and push them into the queue, which is. Next, we remove the node two from the front of the queue and visit the unvisited neighbors and push them into the queue, which is four. Next, we remove the node three from the front of the queue and visit the unvisited neighbors and push them into the queue. All the neighbors of node three have been visited, so we proceed to the next node ten. We remove the node four from the front of the queue and visit the unvisited neighbors and push them into the queue again, all neighbors of node four have been visited, we proceed to the next node into the queue. After this, we can see that all nodes are visited. The next searching technique is depth first search or DFS. In DFS for a graph, we traverse all adjacent vertices one by one. When we traverse an adjacent vertex, we completely finish the traversal of all vertices reachable through the adjacent vertex. This is similar to a tree where we first completely traverse the left sub tree and then we move to the right sub tree. The key difference is that unlike trees, graphs may contain cygle a node may visited more than one. In this example, initially, the stack and the visited arrays are empty. First, we visit zero and put its adjacent nodes which are not visited yet into the stack. Those are one, two, and three. Now node one is at the top of the stack, we visit now that node one is at the top of the stack, we visit node one and pop it from the stack and put all of its adjacent nodes which are not visited into the stack, which includes two and three. Node two is at the top of the stack, we visit node two and pop it from the stack and put all its adjacent nodes which are not visited into the stack, which include four at three neck. Node four is at the top of the stack, we visit node four and pop it from the stack and put all of its adjacent nodes which are not visited into the stack, which include three. Now Node three is at the top of the stat, so we visit node three and pop it from the stack and put all of its adjacent nodes which are not visited into the stack, which is none left. Now that the stack becomes empty, which means that we have visited all the nodes, and our DFS traversal ends over here. Next, we move on to shortage path algorithm, which is also known as the Digitras algorithm. Idea here is to generate a SPT, also known as shortest par tree with the given source as root. We also need to maintain one set that contains all the vertices included in the shortest p tree, and the other set includes vertices that are not yet included in the shortest path tree. At every step of the algorithm, we need to find a vertex that is in the other set and has a minimum distance from the source. Understand the digit stars algorithm. Let us take a graph over here and find the shortest path from sources all nodes. The algorithm will generate the shortest path from node zero to all other nodes this graph, we will assume that the weight of the edges represents the distance between the two nodes. As we can see here, we have the shortest path from node zero to node one, node zero to node two, from node zero to node three, node zero to node four, and node zero to node six. Initially, we have a set of resources. The distance from the source node to itself is zero. In this example, the source node is zero. The distance from the source node to all other nodes is unknown, so we will mark them as infinity. We also have an array of unvisited elements that will keep track of unvisited or unmarked node. The algorithm will be completed when all the nodes are marked as visited and the distance between them is added to the par. Currently, the unvisited nodes are 01235. In step one, we are going to start from node zero and mark the node as visited in red color. Next, we are going to check for adjacent nodes. Now we have a choice over here. Either we can choose node one with distance two or we can choose node two with a distance of six. We are going to choose the node with a minimum distance. In this step, node one is the minimum distance adjacent node, so it is marked up as visited and it adds up the distance. So currently node zero to node one is equal to two. Then we move forward and check for the adjacent node which is node three. So it is marked as visited and we add up the distance and now the distance from node zero to node one to node three is going to be two plus five, which is seven. In the next step, again, we have two choices for adjacent nodes. Either we can choose node four with distance ten or either we can choose node five with the distance teen. Again, we have to choose a node with the minimum distance. Here we are going to choose node four and mark it as visited. The total distance from node zero to one to three to four is going to be two plus five plus ten, which is 17. In step five, again, we move forward and check for adjacent node, which is node six, where is marked as visited and add up the distances from node zero to one to three to four to six, which is two plus five plus ten plus two, which is 19. The shortest distance from the source vertex is 19, which is the optimal real life example of graphs would be social networks. Social networks like LinkedIn represent people as nodes and connections are their edges, forming a graph structure to eco their relationship. The next advanced data structure is hash table. A hash table is defined as a data structure that is used to insert, look up and remove key value pairs quickly. It operates on a hashing concept where each key is translated by a hash function into a distinct index in an array. The index function as a storage location for its matching value. In simple words, it maxs the with the value. A real life example would be a dictionary that map words to their meaning, just like a hash table which maps it keys to its value for quite cuts. So by now, we have covered trees, binary trees, binary search trees, heaps, graphs, and hash tables along with their applications in real life scenarios. Understanding these data structures will equip you with the necessary tools to solve complex programming challenges efficiently. Thank you for joining and happy learning 15. Algorithm Basics: This lesson, we will introduce the fundamental concepts of algorithmic complex including time and space complexity. Understanding these concepts will help you evaluate the efficiency of algorithms and choose the right approach for solving problem. What is an algorithm? An algorithm is a step by step process or set of instructions to solve a problem. It can be as simple as a recipe for cooking a dish or a complex problem like sorting millions of pieces of data in a computer. An example would be, imagine that you have a list of names and you want to find a particular name. Steps you take to search the name will form an algorithm. Why do we need to analyze algorithms? Not all algorithms solve a problem in the same way. Some algorithms are faster and use fewer resources while others say it longer and conviw more memory. By analyzing algorithms, we can determine how fast an algorithm runs, which is also known as time complexity. We can estimate how much memory an algorithm is going to use, which is known as space complexity and also compare different algorithms to choose which is the most efficient one. Algorithm efficiency. The efficiency of an algorithm depends on two main factors. The first one is time efficiency, how fast an algorithm runs. The second one is space efficiency, which means how much memory the algorithm meets. Choosing an efficient algorithm helps programs to run faster and use fewer resources, which is especially important for large scale instead of running an algorithm on different devices to see how fast it works, computer scientists use theoretical analysis to measure its performance. This analysis focuses on how the algorithm behaves as the input size grows and also estimating the number of operations needed rather than the exact time. Next, we move on to time and space complexity. What is time complexity? Time complexity refers to how long an algorithm takes to run based on the size of the input. It helps us understand how the execution time increases when the amount of input data grows. An example would be if searching for a name in a list of ten names takes 1 second, searching in a list of thousand names will take more time. The different types of time complexity cases would include the best case, which is the minimum time an algorithm takes. Example, finding the first item in a list, the worst case, which is the maximum time an algorithm takes. Example would be searching for an item that isn't present in the list. The average case would be the expected time algorithm takes for a ran algorithms can have different time complexities depending on their design. The first one is big 01. The algorithm takes the same amount of time regardless of its input size. The time is constant. An example would be accessing an element in an array. Then have big O log N. The algorithms run time increases slowly, even as the input size grows significant. An example would be binary sir. Big O L, the execution time grows proportionally with the input size, also known as linear time. Here, the example can be linear sir. Then we big O N square. The execution time increases rapidly with the input size increase, making it slow for larger input. An example would be of sort. The last one is big 02 stro the power N. The algorithm takes an extremely long time to run as the input size grows. Also known as exponential an example would be solving combination problem. What is space complexity? Pace complexity refers to how much memory and algorithm needs to draw. It includes bol fixed memory, which is used for variables, constants, and program instructions. Then we have dynamic memory used for storing input data and additional processing. Example would be sorting a list of thousand names might need extra memory to store temporary values during the factors affecting phase complexity include the input size. More data requires more memory. We have the data storage storing large data structures. Example, arrays and list increases space usage. Then recursion. Recursive algorithms consume more memory to keep track of function calls. Some common phase complexity include bigger notation one. The algorithm uses a fixed amount of memory regardless of the input size, also known as con an example would be swabbing two numb. We have big O notation N. Here the memory usage grows in direct proportion to the input size, also known as linear space. An example would be storing elements in an array. Then we have bigger notation N squared where the memory consumption increases significantly with larger inputs and also known as dried spaces. This will have an example of matrix operation. Balancing time and space complexity is input. Real world applications, there are often trade offs between time and space complexes. So algorithms run faster but require more memory, while others take less memory but run slower. An example would be a sorting algorithm may run quickly but use extra space to store temporary values. Another example would be another sorting method we use no extra space but takes longer to come. Rousing the right algorithm depends on the available memory and processing speed that is required. Concluding over here, algorithmic complexities help us to measure the efficiency of any algorth. The time complexity focuses on the execution speed while these complexity looks at memory usage. Understanding these concepts help in choosing the best solution for solving real world problems efficiently. Thank you for joining me, and I'll see you in the next lesson. 16. Searching and Sorting Algorithms: This lesson, we will explore some of the most fundamental algorithms used in programming searching and sorting. These are the essential concepts that help us efficiently manage and retrieve data from large collections. Searching is a process of finding a specific item in a list or a collection of elements. The two most common searching techniques are linear search and binary search. Linear search is the simplest way to search for an item. In this method, we go through the list one element at a time from the beginning to the end until we find the desired or reach the end of the list. How does this algorithm work? We start from the first element of the list. We compare it with the target value. Then if it matches, the search is successful. If it does not match, we move to the next element and repeat. If you reach the end of the list without finding the item, the search is an unsuccessful search. An example for linear search would be, imagine you are searching for your favorite book on a shelf by checking each book one at a time from left to right. The advantages of linear search is that it is simple to implement and it works very well with small dataset. The disadvantage of linear search is that it is very slow or large lists because it checks the best case scenario is when the item is found at the first position or the beginning. The worst case scenario is that the item is not in the list or it is present at the last position. Next, we move on to binary search. Binary search is much faster method, but it works only on a sorted list. Instead of checking each element one by one, it divides the list into two halves and search and searches it more efficiently. How does binary search work? It starts at the middle of the list. If it matches the target, the search is successful. If the target is smaller, it focuses on the left half of the list. If the target is larger, it focuses on the right half of the list. It repeats the process until the element is found, or the search space is empty. An example, sola would be looking for a word in the dictionary. You open the book in the middle and check whether to move left or right. The advantages of a binary search is that it is much faster than linear search and also it reduces the number of elements to check at each step. The disadvantage of binary search is that it only works on sorted data, and also it is more complex to implement compared to linear search. The best case scenario of binary search is that when the item is found in the middle immediately, the worst case scenario is that the item is not in the list after repeatedly dividing the list. A process of arranging data in a particular order, such as ascending, small to large or descending, which is large to small. Sorting helps with easier searching and better organization of data. The most common sorting algorithms are bubble sort, insertion sort, merge sort, and quid sort. Bubble sort is one of the most simplest sorting techniques. It repeatedly compares adjacent elements and swaps then if they are in the wrong order. The working of a bubble sort includes starting at the beginning of the list. Then we compare the first two elements and swap if needed. Then move to the next pair and repeat until the end of the list. The largest element bubbles the end of each pass. Repeat the process for the remaining unsorted part. Example scenario would be imagine that you are sorting a row of people by height by repeatedly comparing and swapping pair standing next to each other. The advantage of a bubble sort, it is very simple to understand and the implementation is very easy. Also, it is useful for smaller data set. The disadvantage is that it is extremely slow for large datasets, and also it takes long time to sort if the list is reverse. The best case scenario is that the list is already the worst case scenario is that the list is in reverse order and requires many swaking. Insertion sort works like sorting a deck of cards. It takes elements one by one and places them in their correct position relative to the already sorted part. The working of insertion sorts includes, start with the second element since the first element is already considered that it is sorted. Compare it with the elements before and insert them in the right position. Repeat for each element until the list is fully sorted. An example scenario will be sorting playing cards in your hand by picking them one by one and placing them at the right spot. The advantages of insertion sort is that it works very well for small lists and nearly sorted list. It is simple and easy to understand. The disadvantage is that it is very slow for large datasets and it is more efficient than bubble sort, but not the best case is that the list is already sorted, and the worst case scenario would be the list is in reverse Odd. Merge Sort is a divide and conquer algorithm that splits the list into smaller parts, sorts them, and then merges them back together. The working of merge sort would include dividing the list into two halves until each part contains a single element. Merge the sorted half pack together in the correct order. Repeat until the entire list has been sort. Example scenario would be imagine sorting two piles of already sorted papers by merging them into one Pi in order. Advantage of merge sort is that it is very fast and efficient for large dataset and it works very well for linked lists and large file. The disadvantage is that it requires extra memory to store the divided lists, and also it is more complex to understand compared to simpler. Best case is that name as the worst case, since it is always divided in list worst that similar to the best case because of its consistent steps. Next, we have quick SOT, which is another divide and conquer algorithm. It selects a pivot element and rearranges the list so that smaller elements go on one side and larger elements go on the other side. The working of quick sort would be picking up pivot element, usually the last or the first element. Arrange the list so that small elements come before the pivot and larger ones go after. Repeat this process for the left and right sub list, combine everything together when fully sort. Example scenario would be sorting a group of people by choosing high as pivot and dividing them into shorter and taller group. The advantage of quick SWOT is that it is fast for larger datasets, and it works very well without extra memory. Disadvantage is that performance depends on a pivot pie. Lower in the worst case is a bad pivot is chosen. Bst case is that the pivot divides the list into equal halves, and the worst case is that the pivot is always the smallest or the large conclusion for searching, use linear search for smaller and unsorted list. Use binary search for sorted lists for faster results. For sorting, bubble sort and insertion sorts are easy but slow. Mode sort and quick sort are faster for larger data set. Understanding these algorithms help improve efficiency and make better decisions while handling data. Thank you for joining me in this lesson, and I will see you in the next 17. Recursion and Backtracking: This lesson, we'll explore two important problem solving techniques used in programming, recursion and backtracking. These concepts are widely used to solve complex problems by breaking them down into smaller manageable parts. What is recursion? Recursion is a problem solving technique in which function calls itself to solve smaller parts of the same problem. The idea is to big task into simpler versions of the same task until we reach a point where the problem is small enough to be solved direct link of recursion as solving a problem in layers where each step brings you closer to the final answer. How does recursion work? Every recursive function must follow two key paths. The best case. This is the stopping condition that tells the function when to stop calling itself. Without a base case, the function will continue calling itself forever causing an error. The recursive case. This is where the function calls its make the problem into smaller versions of the same problem. Trying to understand this in a simpler way would be an example of climbing a ladder. Imagine you are trying a ladder, you take one step at a t until you reach the top, which is the base. Each step depends on the previous one, just like the recursion. Each step salts a smaller version of the problem. Recursion is widely used in many programming scenarios, including solving a mathematical problem, like calculating factorials, computing fibonaki series, solving problems like Tower of Hanoi problem, moving discs between tegs, pseudoco solving using trial and error method, working with data structures like searching and traversing trees, navigating graphs, divide and conquer algorithms like sorting algorithms like merge sort and quick SOT, breaking down larger problems into smaller sub advantages of recursion. It makes the complex problem easier to understand and solve. It reduces the need of repetitive code by breaking the task into smaller steps. It is useful for problems that naturally divide into sub problem, but there are disadvantages to recursion, which include that it can be inefficient in terms of memory usage because each function call takes up space on the call stack. It may lead to infinite loop if the base case is not correctly defined. Also, sometimes it is slower in compared to iterative solution. Problem solving use tracking. So what is backtracking? Backtracking is an advanced problem solving technique that is used to explore all possible solutions to a problem. Backtracking is an advanced problem solving technique that is used to explore all possible solutions to a problem by trying different choices and undoing, which is backtracking, the ones that don't theory to the solution. It works by exploring possible paths in a decision tree. Backtracking when a solution path fails, trying a different option instead. So in backtracking, we start with an initial choice. We explore the possible options based on that choice. If an option works, continue exploring it further. If an option fails, go back, also known as backtrack and try a different choice. Repeat until a valid solution is found or all options are exhausted. To understand backtracking a little bit more, we use an example of salving a maze. Imagine you are trying to find your through the maze. You try different paths and if you reach a dead end, you go back to the last intersection and take a different route. This is exactly how backtracking works, trying different possibilities until the right one is found. The applications of backtracking include salting puzzles like pudoco crossword puzzles. Queens problem. Combinatory problems like generating all possible combinations, subset generation, searching problems, like path finding in a maze or grid based game, also finding all possible routes in a travel it. The steps in a backtracking solution would include some general steps. The first one B, choose, select an option from the available choices. The next would be explore, move forward and explore possible next steps. Third would be check. If a condition is violated, backtrack and try another option. The fourth one is repeat. Continue exploring until a valid solution is found. Or all options are exhausted. The advantage of backtracking includes that it helps solve complex problems that require trying multiple possibilities. Also, it provides an efficient way to find solution without brute force searching. Third is that it can be optimized to discard unpromising paths early on disadvantages of backtracking is that it can be slow for large problem spaces with many possibilities. It may explore redundant parts if not optimized properly, and also it requires careful implementation to avoid excessive recursion depth in conclusion. Recursion is useful when a problem can be divided into smaller versions of etc Backtracking is useful when we multiple options and undo choices that don't lead to a solution. Both the techniques are powerful tools for problem solving and are widely used in programming, especially for algorithms involving decision making and exploration. Thank you for joining me, and I will see you in the next module 18. Software Development Lifecyle (SDLC): So in Module four, we are going to study about software development and engineering. In the first lesson, we are going to introduce the concept of software development life cycle, also known as SDLC and explore the different SDLC models. We'll also break down each phases of SDLC to help bigners understand how software is planned, developed and deployed. So what is SDLC? The software development life cycle is a structured process that is used to develop high quality software. It provides a step by step approach to create software, ensuring that the final product meets user requirements and it is delivered efficiently. Key objectives of a software development life cycle is to ensure that the software meets the customer needs. It should reduce the development cost and time. It should improve quality and efficiency and it should provide a clear development map several models exist to implement SDLC, each with a different approach to organizing the development processes. Some popular SDLCs include the Waterfall model. The Waterfall model is a sequential approach where each phase must be completed before moving to the neck. It's easy to manage, but lacks the flexibility if changes are needed in the later stages. The first stage of Waterfall model is gathering the requirements, then designing the software, implementing the software, testing the software, after testing, deploying the software, and then at the end, we maintain the soft advantages of the waterfall model is that it is very simple and easy to understand. It has a clear structure with well defined stages. It is suitable for smaller projects and well defined requirements. The disadvantage is that it is difficult to make changes once a phase is completed and it is not suitable for complex and evolving projects. The next model is the agile model. Agile model is an iterative and flexible approach where the development is done in small cycles called iterations or sprint. Agile promotes collaboration and adapts to changes very quickly. Here, we first plan, then design, then develop the software, we test the software, we release it, gather feedbacks from the software, and if any changes are required, we go back to planning how to make those change advantages of Agile model is that it encourages the customer collaboration. It allows changes and improvements during the development stage. It is faster in delivery of functional software. The disadvantages of Agile model is that it requires frequent communication and involvement and it may lack proper documentation. The Scrum model is a subset of the Agile model, focusing on delivering small parts of the software in fixed time periods called sprints, usually two to four weeks. The Scrum team collaborates daily to track their progress. First stage is where the initial list of project requirements is made, then the planning work of creating a sprint is created. Actual coding and building of product is done, then evaluating the work done during the sprint period and then releasing it, deploying the product to the user. Advantages of Scrum model is that it is well suited for dynamic projects with evolving requirements, car roles and responsibilities like product owners Scrum Master development team continuous feedbacks are present and improvements can be made according to the feedbacks. The disadvantage is that it requires discipline and team collaboration, and it may not work for highly structured environment. Regardless of the moral used in software development life cycle, generally, it consists of the following phases. The first phase being planning. In this phase, the project goals, scope, timeline, budget are defined, stakeholders gather together to discuss requirement and create the initial project plan. The key activities that are done at this point of time is defining the project goals, estimating the cost and timeline, and identifying all the risks that are present. The next stage is designing. This phase focuses on creating the systems architecture and design. It outlines how the software will work, including the data flow and user interfaces. The key activities that are done at this stage is creating wireframes and prototypes, choosing the technology staff and also designing the database. The third stage is coding, also Lola's implementation in this phase developers actual code to build the software. It involves programming based on a design specification. The key activities the are writing and reviewing the code, integrating various components, ensuring functionality matches the design. The next stage is testing. After coding, the software is tested to identify and fix any bugs. Various testing techniques are used to ensure that the system works correctly. Activities done are unit testing, testing individual parts, integration testing, checking module interactions, and user acceptance testing, which is validating with the users. The next stage is deployment. Once testing is successful, the software is deployed to users. Deployment can be done in stages, phased deployment or all at once, which is full. Activities done at this stage is setting up a production environment, monitoring system performance, and training users if needed. The last stage is maintenance. After deployment, the software requires regular updates and maintenance to fix issues, add new features, and ensure that smooth operation is going on. The key activities here are fixing any bugs and ors, updating softwares for improvement, and providing customer concluding this lesson, Software Development Lifecycle is a structured approach to software development that ensures quality and efficiency. Various models such as waterfall, Agile and Scrum cater to different project needs. The key phases of software development life cycle include planning, designing, coding, testing, deployment and maintenance. By understanding these concepts, beginners can get a strong foundation in software development process. Thank you for joining me today, and I'll see you in the next lesson. 19. Debugging and Testing: This lesson, we'll cover debugging and testing. In the world of software development, debugging and testing are two essential steps to ensure your code works karaki. Debugging helps you to find and fix errors while testing ensures that your code behaves as expected. In this lesson, we will break down common debugging techniques and introduce you to unit testing and integration testing using Python's unit test framework. These concepts are crucial for writing reliable code and preparing it for production. Debugging is a process of identifying and fixing issue in your code. It is like solving a puzzle and it can be challenging. Here are some common techniques that will help you. The first one is print statement. One of the easiest way to debug your code is to insert print statements. This allows you to see the values of variables at different stages of your program. By examining the output, you can identify where things go wrong. For example, if you're working with a function and suspect an issue with its parameters, you can print out the values at the start of the function using a debugger. Debugger is a tool that allows you to step through your code line by line, inspecting variables and program flow. Pitl has a built in debugger called PDB. Using PDB, you can pause the execution of your program at any point and interact with it, which helps to see what's going on inside. To use PDB in Python, we have to import the PDB at first and then use the pd dot set trace function. This will pause your program and let you inspect variables, step through the code, or even change the flow while debugger. Next debugger is logging. Sometimes print statements are not enough, especially when working with large applications. Logging allows you to record what's happening in your program at different levels. Example, info, warning, errors, et cetera. It's helpful for tracking issues in both development and production environment. You can use the Python's built in logging module to write, a file or a console so that you can review them later. Then comes checking for syntax error. A common mistake when debugging is not checking for simple syntax errors. Python will often point out syntax mistakes when you run your code, but some are harder to spawn, looking for missing parenthesis, indentation errors, and typos. Tools like ilters can also help you catch these before you run your program. The next one is isolate the debugging a complex program, try isolating the part that is causing the issue. If you are working with multiple functions, test them individually. This helps you to pinpoint exactly where things are going wrong. Unit testing is a process of testing individual components or units of your code to ensure they are working correctly. These components could be functions or methods that perform specific tasks. What is unit testing? Unit tests focus on small isolated pieces of functionalities. Each test typically checks whether a function or a method returns the expected output for a given text can be written before or after the development of the code. This is known as test driven development or TDD. Python provides a built in framework for writing and running tests called unit test. It allows you to write test cases, group them, and run them automatically. What you need to know about unit testing is the first one, which is test is a single unit test. It consists of a set of inputs and the expected output. Test suits, a collection of test cases. Then we have assertions. These are statements that check if your code produces the expected outputs. So how to write a unit test? First, we import the unit test module. Then we create a class that inherits from unit test to test case. Inside this class, we write methods that begin with test underscode example, test uncode addition or something like that. Use assertion in each method to check if the function you are testing behaves correctly or not. Here is an example of test ad method that checks if add function produces the correct result for several outputs or run your unit tests, simply execute the command. To run your test, simply execute the following command in the terminal. This will run all the test cases in your file and you will see a report showing which tests passed or failed. Why unit tests check individual component, integration tests check how multiple components work together. Integration tests ensure that the different parts of your program interact correctly when combined. What is integration testing in integration testing? You test how different modules or functions communicate and work with each other. Important because even if individual components work fine in isolation, they may behave unexpectedly when they are combined. For example, let's say we have two function add, which adds numbers and multiply, which multiplies the numbers. We could write an integration test to check how they behave together in a larger function. Here, the test complex operation method checks if combination of add and multiply work as they are expected to key difference between unit test and integration tests are that unit testing tests small individual components of your code. Integration testing tests the interaction between multiple compreent. In conclusion, debugging and testing are keys to building reliable and maintainable code. Debugging techniques like print statements using debuggers, logging and isolating the problem can help you find and fix issues. Unit and integration tests help you ensure that your code works correctly, both in isolation or when they are combined with other components. Mastering debugging, you will become a better programmer and be able to write cleaner and more reliable core. Thank you for joining me and I will see you in the next lesson. 20. Software Design Principles: This lesson, we are going to study about software design principles. When you write a code, especially for a larger project, it's important to follow certain design principles to ensure that your code is clean, manageable, and scalable. These principles help you create software that is easy to understand, modify, and extend over the time. In this lesson, we will explore concept of solid principles, a set of guidelines for object oriented design, as well as some design patterns which are common solutions to recurring design problems. The solid principles are set of five Ebotan guidelines that help developers design better object oriented system. These principles ensure that the software is more flexible, easier to maintain, and scalable. Let us break them down. The first one is S, single responsibility principle or SRP. The SRP states that a class should have only one reason to change. In simple terms, this means that each class should be responsible for only one job or task. If a class has more than one responsibility, it becomes more complicated and harder to maintain. For example, if you have a class that handles both user authentication and sending email notification, these are two separate responsibilities. Instead, it would be better to separate them into two different classes, each handling one responsibility. The next one is O. Open or closed principle also known as OCP. The open closed principle says that a class should be open pod expansion but closed for modification. This means that you should be able to add new functionalities to a class without changing its existing code. For example, if you have a payment processing class that can handle credit card payments, you should extend it to support PPL payments without changing your original truss. You would do this by adding new classes that attend the functionality rather than modifying the existing one. The next one is is list of substitution principle LSP. The list of substitution principle states that the object of a subclass should be able to replace object of the parent class without affecting the correctness of the program. In other words, if a class B is a subclass of class A, you should be able to use class B anywhere class A is used and the program should still work correctly. If subclassing breaks this rule, it can cause unexpected behavior. The next one is I interface segregation principle ISP. The interface segregation principle suggests that a class should not be forced to implement interfaces it does not use. Instead of one large interface with many methods, it is better to have smaller, more focused interfaces. For example, if you have a class that implements an interface with methods for both printing and scanning documents, but your class only needs to handle the printing, then it shouldn't be forced to implement scanning methods. Instead, create a separate interface for printing and scanning. The next one is dependency inversion principle. Also the DIP states that high level modules should not depend on low level modules. Instead, both should depend on abstraction, that is interface. Additionally, abstraction should not depend on detail, but details should depend on abstraction. This principle encourages you to design systems where the detail of how things work are hidden behind abstractions, making it easier to change the details without affecting the overall structure of the next we move on to design patterns. Design patterns are reusable solutions to common software design problems. These patterns help you avoid reinventing the wheel and can make your code easier to understand and maintain. There are many design patterns, but in this lesl we'll cover some of the most commonly used Singleton, factory, and observer. Singleton pattern. The Singleton pattern ensures that a class has only one instance throughout the entire program. It is useful when you want to control access to a shared resource such as a database connection or a logging service. For example, imagine you have a class that manages the database connection. Don't want to open multiple database connections, so you use Singleton pattern to ensure that only one connection exists, and all parts of your program use this single instant the factory pattern. The factory pattern is a way to create objects without specifying the exact class of the object that will be created. Instead of instantiating a class, you create a factory method or a class that decides which object Dan tier. For example, suppose you have a class that creates different types of vehicles like cars and bikes, rather than directly creating the vehicle in your code, you use factory to decide which type of vehicle to create based on a certain condition. Example, user input. The benefit of this pattern is that it allows your code to be more flexible and extendable. You can add new types of vehicles without changing the existing. Then we have the observer pattern. The observer pattern is used when you want to allow an object, subject to notify other objects, which is the observers when its state changes. It's a great way to handle situation where one objects need changes to be reflected in multiple places. For example, imagine that you are building a web application. The web station is the subject here which updates its data and multiple displays, which is the observer needs to update to reflect the new data. Instead of having the weather station Dialecty update each display, you use the observer pattern to allow display to subscribe to the changes in the weather station. Whenever the weather station changes all subscribed displays are notified automatic. In conclusion, understanding software design principles like solid and common design patterns can help you write better and more maintainable code. These principles are important for building scalable and flexible software systems. By following the solid principle, you ensure that your code is more modular easy to extend. Design patterns on the other hand offer well established solution to common design challenge. As you continue learning and writing codes, try to incorporate these principles and patterns in your work. They will not only make your code cleaner and more efficient, but also help you think about software design in a more structured way. With these tools in your toolkit, you are on your way to become a more effective and thoughtful developer. Thank you for joining me, and I will see you in the next module. 21. Introduction to Databases: Welcome to Module five, databases and EQUAL. In today's lesson, we will explore introduction to databases. We will explore what databases are, why they are important, and the different types of databases. Let us start. What is a database? A database is like a digital filling cabinet. It is a system that allows us to store, organize, and retrieve data efficiently. Think of it as a library where books or data are stored in an organized way so that you can find them quickly when needed. For example, imagine that you are an online store, you need to keep track of all the products, customer orders, and inventory. A database helps you to store all this information in one place and retrieve it easily whenever required. Why do we use database? We use databases because they make managing large amounts of data simple and efficient. Here are a few reasons why databases are essential. The first one is organization. Databases store data in unstructured way, making it easy to find and use. The second one is efficiency. They allow quick access to data even when dealing with millions of records. Security reasons, databases protect sensitive information by controlling who can access or modify the data. The fourth one is consistency. They ensure data is accurate and up to date across all systems. For instance, when you log into your social media account, the app uses a database to fetch your profile information, posts, and messages instantly. Without a database, this process will be slow and kt types of databases. Databases come in different types, but the two most common are relational database and non relational database. That is break them down. Relational databases store data in tables like the spreadsheet. Each table has rows and columns. Rows represent individual records and column represents attributes of those records. For example, imagine a table for storing customer information. Each row represents a customer. The columns might include customer ID, name email and phone numbers. Relational databases use language called SQL or structured query language to manage and query the data. They are great for handling structured data with clear relationship. A real life example is a bank's database. It uses tables to store customers accounts, transactions, and balances, and SQL to retrieve the information like your account balance or transaction the non relational database, also called as no CIL database, store data in a more flexible way. Instead of tables. They use formats like documents, key value pairs or graphs. For example, a document based database might store the data something like this. Non relational databases are ideal for handling unstructured or semi structured data like social media posts, sensor data, or product catalogue. Real life example is a social media platform. It uses non relational database to store posts, comments and user interaction which don't fit neatly into tables. Relational databases versus non relational databases. Let us compare these two. Relational databases are best for structured data with queer relationship. Examples would include M SQL, postrgEQL and Oracle. The non relational databases are best for unstructured or semi structured data. Example, MongoDB, Cassandra, and Redis. Choosing the type depends on the kind of data you are working with and the problem you are trying to solve. Let's recap what we have learned. A database is a system for storing and managing data. We use databases to organize, secure and efficiently access the data. There are two main types of databases, relational, which are structured and table based and non relational, which are flexible and document based. Understanding databases is the first step towards working with data efficiently. Whether you're building an app, analyzing data or managing the business, databases are at the heart of all of it. So that's it for today's lesson. Thank you for joining, and I'll see you in the next lesson. 22. SQL Basics: Welcome to the SQL basic lasset. In this session, we will cover the fundamentals of SQL, including understanding the SQL syntax, performing crude operations that is C for create R for read, U for update, and D for delete. SQL, which is structured query language is a powerful tool used to manage and manipulate relational databases. By the end of this lesson, you will have a solid foundation to interact with the databases effectively. So what is SQL? SQL is a language that is used to communicate with databases. It allows you to store, retrieve, modify, and delete data in a structured way. Think of databases as a digital filling cabinet and SQL as a tool that we use to organize and manage the files inside it. Let us start with the basic SQL syntax. SQL statements are written in a specific format, so let us break it up. First, let us start with a keyword. Keywords are the reserved words like select, insert, update, and delete that perform specific actions. We have tables Data in the database are stored in tables which are like spreadsheets with rows and columns. Then you have the clauses, additional conditions or filters such as we order by and limit. Here is an example of a basic sent pack. In this example, select is the keyword to retrieve the data. Column underscore name is the specific column that you want to ret table name is the table where the data is stored and the we clause is to filter the results based on the condition that is followed by. In this real life example, imagine that you have a table called employees with column names like name, age, and department. If you want to find out the names of all employees in the sales department, you would write select name from employees where department is equal to sales, followed by a semicm. Then we move on to crude operations. Crude stands for create, read, update, and read. These are the four basic operations that you can perform on data in the database. The first one is create, also known as insert. The insert statement is used to add new data to the table. Here is the syntax of the insert statement where insert into is the keyword followed by the table name and the column names, and values is also a keyword followed by the values that need to be inserted into the column. Then we have the crude operation, which is also known as the select statement that is used to retrieve data from the table. The syntax of a select statement is written by using the select keyword followed by the column names that need to be retrieved. The From statement is followed by the table name from which the columns need to be retrieved. Then in case we need any conditions, we write the statement. We have the update operation. The update statement is used to modify existing data tables. The syntax of it is using the keyword update followed by the table name that needs to be updated. Then we need to set what needs to be changed. So here we use the set keyword and column one is equal to value one. Column two is equal to value two and so on. Also, we can add whatever conditions we need over here. The delete statement is used to remove data from the table. We use the delete keyword followed by from, and then the table name from which it needs to be deleted and the were condition is also there so that you can delete specific data. Putting it all together, let us practice with our real life scenario. Imagine that you are managing a database for a bookstore and how we are going to use the crude operations over here. I am using SQL light online for executing the queries. Here, I have created a table called Books, which has columns called ID, title, author, and price. The first operation that we are going to do is create. We are going to add a new book to the books table. So here so here for the first entry into the books table, I'm using the insert into statement. We are going to insert in the books table in the columns ID, title, author and Price, we're going to add values hundred SQL for Bigness by the author Alice Johnson, and the price of the book is 29.9. Let us run this code. You see that we have not encountered any errors. That means that our data has been successfully entered into our books table. Let us now retrieve all the books that are written by Alice Johnson. For that, we are going to write the select statement over here. In my select statement, I want to retrieve the title and the price from the books table where the author's name is Alice Johnson. If we run this, we are going to get an output on our screen, and we had the title over here. The book's title name is EsquLF Big ners and the price is 29 99. Let us perform the crude operation update over here. Let us increase the price of EQULF Bg neurs to $34.34 $99. So for that, we are going to write the update statement. Update. We are going to write update the books table, set the price at 34 dot 99, where the title of the book is SQL sopping nose. If we run this and we don't get any error, that means that our update has successfully been done. Let us retrieve the data to check if our update has been made. As you can see, over here, the table has been updated and we have the price of 34 dot 99. Let us perform the last crude operation that is deed. Currently, we just have one record in this table, so let us add two, three more records and then we'll delete one of we are going to use the insert statement again to add few records to this particular table. Here in our insert statement, we are going to insert into books the columns ID title, author and price. We're going to enter two values. So let us run this. And because we have not encountered any error, that means that our insertion has been performed successfully. If we check over here, we have our values in this table. Now let us delete from this particular table. To perform deletion, we are going to use the delete statement. You're going to use delete from books, you're going to write a condition over here where title. Q data analysis. Over here, it is going to delete this particular record. Let us run this. We check our record again. We are not going to get that particular entry of data analysis. A quick recap over here. SQL syntax, we understand the structure of SQL statements using keywords, tables, and clauses. The crude operations create, we use in search to add a new data. In read, we use select to retrieve the data. In update, we use update to modify existing data. In delete statement, we use delete to remove the data. So a practice exercise that you can do over here is that you can create a table called students with columns of student ID, name, eight, and grade. You can insert three student records into the table. You can retrieve the names of all students in grade ten. Then you can update the grade of a student named Emily Brown to 11. You can delete the record of a student named Michael Lee. So you have learned the basics of SQL syntax and crude operation. With these skills, you can start managing queering databases effectively, keep practicing and soon you'll be able to handle more complex SQL tasks. Thank you for joining me and I will see you in the next lesson. 23. Database Design and Normalization: Hello, everyone. Welcome to today's lesson on database design and normalization. In this session, we learn how to create database schemas and tables and understand the concept of normalization and denormalization. These are fundamental concepts that will help you design efficient at organized databases. So let us direct. What is a database schema? A database schema is like a blueprint for a database. It defines how data is organized and how relationships between data are structured. Of it as a layout of a house, just like a house has rooms, doors and windows. A database schema has tables, columns, and their relationships. What is a table? A table is where the data is stored in the database? It is like a spreadsheet with rows in column. Each row represents a single record, for example, customer, and each column represents a specific attribute, example, name, email, phone number, et cetera. Let's say we are designing a database for an online bookstore. This schema's name is bookstore, and the tables that we will create are books, customers, and orders. So let us create our tables over here. We have created the books table that stores information about the books. Example, title, author and the price. Then we have the customer's table that stores information about the customer like their name, email address, and their address. Then we have the table for the orders that store information about the orders like order ID, customer ID, book ID, and their order date. The primary keys are the unique identifiers for each of the record in the table over here, we have book ID, customer ID, and order ID. Then we have the foreign keys over here. Foreign key is basically a column that links two tables together. Example, customer ID in the order table links to the customer's table over here, and the book ID in the orders table links to the bookstable. Our code has run successfully over here. Then let's move on to normalization and denormalization. What is normalization? Normalization is a process of organizing data in a database to reduce redundancy and improve data integrital. It involves breaking down large tables into smaller related tables and defining relationship between them. Why normalize the data to avoid duplicate data example storing the same customer's name multiple times to make database easier to maintain and update. Next, we have the normalization levels. The first normal form, which is also known as the one NF in one NF, each column should contain atomic that is indivisible values. Each row should be unique. Example, in the books table, each book should have only one author. If a book has multiple authors, it should be split into another table. The second normal form, the two NF. The table must be in one NF. All the non key columns should depend on the entire primary key. Example, in the order table, order date depends on order ID, not on customer ID or the booked. The third normal form, which is the three NF, this table must be in two NF. There should be no transitive dependency, that is a non key column should not depend on other non key columns. Example, if a customer table had a city and a country column and the country dependent on city, we would move country to a separate table. Let us take a real life example. Imagine a school database. Instead of storing student details and course details in one table, we split them into the student table, which has student ID, name and address, the courses table, which has the course ID and the course name, we have the enrollment table, which has the scud ID, course ID, and the enrollment date. This ensures that the data is not duplicated and it is easy to understand. Next, let us move to denormalization. What is denormalization? Dnormalization is a process of combining tables to improve the read performance, even if it means duplicating data. It is the opposite of normalization and is used in scenarios where the read speed is more important than the writes. When do we use denormalization? When dealing with larger datasets and complex queries, example in data warehouse or reporting system. When read operation is significantly more frequent than the ride operation, at this point of time, we like to use denormalization e commerce website, you might denormalize the order stable to include customer names and book titles directly. Even though this is duplicates data, this speeds up the query and display the order history. Let us create a denormalized table over here. Let us run this. And we have a denormalized table also added over here. There are pros and cons added to our denormalization. The pros are that it is a faster read operation and it is simpler queries. But the cons are that it causes data redundancy, increase storage requirements, and harder to maintain data consistency. Conclusion, a schema is a blueprint of the database. Table is used to store data in rows and columns. Normalization organizes data to reduce redundancy and improve integrity, follows the one NF two NF, and three NF rules. Be normalization combines tables to improve the read performance. It is useful in read heavy systems, but comes with its own read offs. Thank you for joining me in today's lesson, and I will see you in the next 24. Advanced SQL Queries: Come to the lesson on advanced EQUAL queries. In this session, we will explore more powerful EQUAL features that will help you analyze and manipulate data more effectively. We'll cover join operations, sub queries, and aggregations and also Windows functions. Each topic will be explained with simple and real life examples to make it easy to understand. Let us start with join operations. Joins are used to combine rows from two or more tables based on a related column there are several types of joins, but we will focus on most common ones that is inner join, left join, and right join. The first one is inner join. What it does, it returns only the rows where there is a match in both the table. Let us see an example over here. Here, the SQL command selects the specific rows or both the tables if the value of customer ID of the customer table and the customer of the orders table matched. As you can see, the inner join excludes all rows that are not common between the two tables. The next one is left joint. It returns all rows from the left table and the matched rows from the right table. If no match is found, null values are returned for the columns from the right table. In this example, the escual command combines the data from the customers and the orders query selects the customer ID and first name from the customers and the amount from the orders table. Hence, the result includes the rows where customer ID from the customer's table matches the customer from the order table. Then we have joined. It returns all the rows from the right table and the matched rows from the left table. If no match is found, null values are returned or the column from the left table. Here in this example, the Cual command selects the customer ID and first name columns from the customer table and the amount column from the orders. The result set will contain only those rows where there is a match between the customer ID of the customer's table and the customer of the orders table, along with all the remaining rows from the orders table. Next, we move on to subqueries. A sub query is a query nested inside another query. It can be used in select, insert, update, and delete statement. First one is sub query in select statement. A subquery can be used to retrieve a single value that is then used in the main query. The sub query in the were clause would be a sub query that can be used to filter rows based on a particular condition. In a sub query, the outer queries result depend on the result of the inner sub query. That is why the subqueries are also known as the steed queries. So this is how the code works over here. Executes the sub query, which is the inner query first and returns the minimum age as 22. Then the execution of the outer query happens and it selects the customer with the age 22. Next, we move on to aggregation. Aggregation are used to perform calculations on a set of values returning a single value. Common aggregation methods are count sum, average, win, and max. The count function counts the number of rows that matches the particular condition. Over here, we have added the count function to the country column and we have also given a condition that the country needs to be UK. Because there are two UK present in the table, we have a count return of two. There we have the sum function. The sum function adds up all the values in the column and returns. In this piece of code, we have used the sum function against the column amount as the total sales. Though as total sales is going to create a new column called total sales and we have the sum as 13,360, which is the total sum of the amount. We have the average column that calculates the average value of a particular column. In this example, we have selected the average of the age column as the average age, and we get a result of the average of the age column of 25.6. The Windows function performs calculations across a set of table rows that are somehow related to the current row. Unlike the regular aggregation windows function do not group the rows into a single output row. The first one is row number. It assigns a unique number to each row within a partition of a results. So over here, we are going to select name department and salary. These are the three columns that we need from the table, and we are performing a Windows operation called row number, and the partition is going to be by the department. As you can see, the department is for finance and sales over here. So within finance, we have the row number as one, two, and three. Those are the first three rows. And the next one is sales. So in sales itself, we have one and two. Those are the row numbers. Next, we have the rank. Rank is basically similar to row number, but it assigns the same rank to the rows within the same value. So in this particular code. You can see over here that rows with the same salary example rummage and sewage are assigned the same rank and the next rank is skip which is rank two due to duplicated rins. Then we have the sum windows function which basically calculates the running total or the cumulative sum. In conclusion, in this lesson, we have covered the join operations, combining data from multiple tables using inner join, left join, and right joint. We have covered subqueries, which is nothing but nesting queries to filter out or retrieve specific. Also covered aggregation, which is performing calculations like count, sum and average on data, have also covered Windows functions which perform advanced calculations like ranking and cumulative sums. These tools will help you analyze and manipulate data more effectively in SQL and practice these concepts with real world data sets to become more proficient. Thank you for joining me in today's lesson, and I will see you in the next one 25. NoSQL Databases: Hello, everyone. Today we are diving into the world of no SQL databases. No SQL stands for not only ESCR and its type of databases which are designed to handle large volumes of unstructured or semi structured data. Unlike traditional ESQL databases which use tables and rows, no SQUL databases are more flexible and scalable, making them ideal for modern applications like social media, ecommerce and real time analytics. Think of it like this. I SQL databases are like a library with strict cataloging system, no SQL databases are like a messy desk where you can throw anything anywhere and still find it quickly whenever you need it. The types of no SQL databases. There are several types of no SQL databases, but today we'll focus on two popular ones. The first one is document stores like Mongo DP. These store data in JSON like documents. Then comes the key value pair store. Example, reds. This stored the data as a key value pair like a dictionary. Let us explore them in detail. Mongo DB is a document oriented no SQL database. Instead of storing data in tables, it stores it in a collection of document. Each of the document is like a JCN object, which makes it very flexible. So in Mongo DB, we can store the employee's information in this manner. And now I will be extracting out everybody who is in the Department of sales. So let us run our code over here. And you can see that we have our output over here where the employee number one and employee number three both belongs to the sales department. A real life use case of Mongo DB would be companies like Ebay and Forbes use Mongo DB to handle their large scale data. For instance, Eby uses MongoDB to manage product catalogs because it can handle diverse product information without requiring a fixed schema. Flexibility is crucial for e commerce platform where products vary. Next up is Redis, which stands for remote dictionary server. Redis is a key value store, meaning it stores data as simple key value pads. It's incredibly fast because it stores data in memory that is run rather than on disk. Redis is often used for caching, alta, analytics, and session knowledge. Twitter is a great example of reds in action. Tal uses redis to handle real time features like tweet counts and user timelines because redis is so fast, it can handle millions of requests per second, making it perfect for high traffic application. The key difference between SQL and no SQL. The first one is data structure. In SQL, the data structure is of tables with rows and columns which are structured. In no SQL, it is documents, key value pairs, and graphs, which is more flexible. In terms of scalability, SQL is vertically scalable. You can add more power to each single server. In no ScuL, it is horizontally scalable. You can add more server. In terms of schema, EQUAL has a fixed schema where you define the structure upfront. In no escuL, it has a dynamic schema. You can change the structure on the fly. The use cases of SQuL it is best for complex queries and transactions in camp banking system. No EQUAL is best for unstructured data and real time applications like social media. Of EQUL as a rigid but precise tool, but no SQuL is a flexible and a fast tool for modern real life example of SQUL and no UL, let us say you are building a ecommerce platform. For product inventory, structured data with fixed attributes like price, SKU, et cetera, you might use SQL for user activity logs unstructured data like clicks, searches, et cetera, you might use NoSQL. This way, you can get the best of both worlds. When do use noSQUL? So when should you use noSQ here are some scenarios. Your data is unstructured or send structured. Well, you need high scalability and performance or when your application requires real time data processing, also when you're working with g data or distributed system, for example, you are building a mobile app that needs to handle millions of users. At that time, no SQL is your best choice. Let us quickly SQL databases are more flexible and scalable. MongoDB is a document store, great for handling structured data. Redis is a key value store, ideal for caching and real time application. SQL and no SQUL have different strengths, and the choice depends on your use. Thank you for joining me in this session, and I hope to see you in the next module. 26. Introduction to Operating Systems: Hello, everyone. In Module six, we are going to learn about operating systems and networking. I welcome you to today's lesson, which is introduction to operating systems. Whether you're using a smartphone, a laptop or even a smart watch, you are interacting with an operating system. But what exactly is an operating system? And why is it so important? Let us dial in and find out. First, let's talk about the functions of an operating of an operating system or an OS for short at the manager of your computer. It's like the conductor of an orchestra, making sure all the instruments or the hardware and the software working together harmoniously. Here is the main functions of your operating system. The first one is resource management. Your computer has limited resources like CPU, memory, and storage operating system ensures these resources are used efficient. For example, if you are streaming music while browsing the web, the operating system makes sure that both the tasks get enough CPU and memory to run smooth. Next one is user interface. The operating system provides a way for you to interact with your computer. This could be through a graphical user interface, which is GUI, like the icons and the menus you see on your screen. Or a command line interface where you type your command. Then we have the file management. The operating system organizes files and folders on your storage devices. It helps you create, delete, move, and find your files very easily. Imagine your computer storage as a library, and the operating system is the librarian who keeps everything in order. The last one is security. The operating system protects system from unauthorized access. For example, it asks for passwords when you login and ensure that the apps don't access the data, they are not supposed. For example, think of your computer as a car. The operating system is like the driver who controls the engine, which is the CPU, the fuel, which is the memory, and the navigation, which is the file system to get to where you need to go. Next, let's talk about processes. A process is simply a program that's running on your computer. For example, when you open a web browser or a word processor, the operating system creates a process for it. What is a process process is like a container that holds all the information needed to run a program. This includes the code, the data that it's working on, and its current state, whether it's running or ZipaS. How does the operating system manage your process? The operating system keeps track of all running processes and allocate resources like CPT and memory to the. It also ensures that the processes don't interfere with each example, if one program crashes, the operating system make sure it does not bring down the hoses. For example, imagine that you are cooking in the kitchen. Each recipe you are following is like a process. The operating system is the chef who makes sure that each recipe gets the right ingredient, which is the resources and doesn't burn, which is crushing by cook ten, we move on to threads. A thread is a smaller unit within a process. Think of it as a sub task that a process can perform. For example, a word processor might use one thread to handle typing and another thread check spelling. Why use thread threads allow a program to do multiple things at one. This makes applications faster and more responsive. For example, a web browser might use one thread to load a web page and another thread to play a video. How does an operating system handle a thread? The operating system shedes a thread to run on the CPO. It switches between threads so quickly that it feels like they are running simultus. This is called multi threading. Imagine you're reading a book while listening to music. Your brain is like the operating system, switching attention between reading one thread and the listening, which is another quickly, it feels like that you're doing both at the same time. Finally, let's talk about multitasking. This is the ability of an operating system to run multiple processes or threads at the same time. It's what allows you to listen to music, browse the web, and check your email all at once. How does multitasking work? The operating system switches between tasks, giving each one a small slice of the CPU time. This happens so fast that it feels like everything is running simultaneously. Different kinds of multitasking. The first one is pre emptive multitasking, where the operating system decides when to switch task. Then we have the co operative multitasking where the programs decide when to give up the control, which is less common piece thing. Imagine that you are a teacher managing a classroom. You quickly switch between helping one student, answering a question from another, and writing on the board. Even though you are doing multiple things, it feels seamless because you're managing your time effect to recap, an operating system is like a brain of your computer. It manages resources, it handles processes. And enables multitasking. Without an operating system, your computer wouldn't be able to do much at all. I hope that this lesson has helped you understand the basics of operating system. Keep exploring and you will discover even more fascinating details about how they work. Thanks for watching, and I will see you in the next 27. Memory Management: Hello, everyone. Today, we are going to dive into an exciting topic called memory management. Think of your computer as a big library, just like a library, have limited space to store books. Your computer has limited memory to store programs and data. But how does the computer manage all this information efficiently? That's what we are going to explore today. We will cover three main topics, how operating systems manage memory using paging and segmentation, virtual memory, and address spaces. How to operating system manage memory. Imagine your computer's memory like a big bookshelf. You have many books which are programs and data, but the shelf isn't big enough to hold all of them at once. The operating system has to decide which book to keep on the shelf and which ones to store temporarily in a storage room like your hard drive. To manage this, the operating system uses two techniques called paging and segmentation. Let's break them down. The first one is paging. Paging is dividing the bookshelf into small fixed size section, call pages. Each page can hold a small part of a book. Similarly, the operating system divides memory into small fixed size blocks, called pages. For example, let's say that you're reading a big novel, but you only have space on your desk for one chapter at a time. You keep one chapter on your desk and store the rest in the drawer. When you finish the chapter, you swap it with the next one from your drawer. In computers, the operating system does the same thing. It keeps the most frequently used pages in the memory that is ramp and store the rest on a this way, even if the memory is full, the computer can still run multiple programs by swapping pages in and out as needed. The second one is segmentation. Unlike paging, which uses fixed size blocks, segmentation divides the memory into variable sized sections called segment. Each segment represents a logical part of the program like the code, data or stack. Think of segmentation like organizing your bookshelf by fiction, non fiction, and reference books. Each category has a different size, but they are gk in computers, segmentations, help the operating system, manage memory more efficiently by grouping related data together. For example, all the code for a program is stored in one segment and its data is stored in another segment. This makes it easier to manage and protect different parts of the program. Now let's talk about virtual memory. Imagine you're writing a research paper and you have dozens of books and notes spread across your desks, drawers, and even your backpack. Even though your desk is small, you can still access everything you need by organizing and swapping items as required. Virtual memory works similarly. It's a technique that makes your computer think it has more memory than it actually does. The operating system creates an illusion of a large memory space by combining RAM and a portion of the hard drive. For example, if your computer has four GB RAM, but need to run a program that requires six GB, the operating system uses virtual memory to store the extra two GB on the hard drive. When the program needs that data, the operating system swarps it back into way, even if your computer doesn't have enough physical memory, it can still run large programs very smoothly. Finally, let's talk about address spaces. Every program running on your computer needs memory to store its data and instruction. But how does the operating system ensure that one program doesn't accidentally override another program's memory? This is where address spaces comes in. Each program has its own virtual address space, which is like a private map of memory. The program thinks it has access to the entire memory, but in reality, the operating system maps these virtual addresses to the physical memory address. Think of it like this. Imagine you are in a big office building. Each employee has their own desk and they only know the location of their desk in terms of their own office map. They don't need to know where other employees desks are. Similarly, each program has its own virtual address space, and the operating system handles the mapping through physical let's tie all of this together with a real life example. Imagine that you are a chef cooking multiple dishes all at pan. Your kitchen counter is like the ram. It has limited space. Your pantry and your frid are like hard drive. They store the ingredients that aren't currently being used. Paging is like chopping ingredients into small uniform pieces so that they fit neatly on the counter. Segmentation is like grouping ingredients by dish. All the veggies for one dish go in one. Spices for another dish go in another. Virtual memory is like using the pantry and the fridge to store extra ingredients when the counter gets boiled. Address spaces ensure that each dish has its own set of bows and utensils, so you don't mix up the ingredients. This way, you can cook multiple dishes efficiently without running out of space or mixing things up. Concluding today's lesson, memory management is like being a librarian, chef, office manager, of your computer. The operating system uses techniques like paging, segmentation, virtual memory, and address spaces to ensure that everything runs smoothly, even when the memory is very limited. I hope that today's lesson has helped you understand how your computer manages the memory. Thank you for joining today's lesson, and I will see you in the next. 28. File System and Disk Management : Hello, everyone. Today, we are going to explore an essential topic called file systems and disk management. Think of your computer storage like a giant filling cabinet. Just like you organize your files into folders and raws, your computer organizes the data into files and directories. But how does it all work? That's what we'll learn today. We'll cover two main topics file structures, directories, and file operations, and also understanding the file provision. Imagine your computer storage is like a big office filing system. You have files, which is document photos and videos at folders, which are directories to organize them. Let us greek them now. The first one is file structure. A file is a collection of data stored on your computer. It could be text document, a song, or even a program. Files are like individual documents in your filing cabinet. Example, think of a file as a single sheet of paper with information written on it. You can have many sheets. Those are files in your filing cabinet. That is your storage. Files are also stored in a specific format depending on their type. For example, a text file might be stored as plain text, an image file might be stored as pixels and colors. A video file might be stored as frames and audio. The way data is organized inside a file is called its file structure. Operating system knows how to read and write these structures, so you can access your files very easily. Then we move on to directories. Now let's talk about directories, also known as folders. Directories are like drawers or sections in your filing cabinet. They help you organize fires into groups. Example, the Q might have a directory for school for all school related files and another one called photos for your pictures. Inside the photo directory, you might have sub directories called vacation and family to further organize your files. Directories make it easier to find and manage your files. Without them, all your files would be in one big pipe and it would be hard to find now that we know what files and directories are, let's talk about what you can do with them. These are called file operations. The most common file operations are create, which is making a new file or directory. You can read, which is opening and viewing the contents of a file. You can write, which is adding or changing the data in a file, you can delete, which is removing a file or a direct you can rename, which is changing the name of a file or a direct. Think of these operations like managing your file cabinet. Create would be adding a new document or a folder. Read would be opening a document to read it. Right would be editing or adding information to the document. Delete would be throwing away the document that you no longer need. Rename will be changing the label on the folder or the doc. These operations are basic ways you can interact with the files and directories on your computer. Now let us talk about file permisels. Imagine you are working in an office, and you have a filing cabinet with sensitive document. You don't want anyone to have access to all the files, some files might be for your eyes only while other files can be shared with your team. File permissions work in the same way. They control who can access, modify or delete the files and the directories. Let us break them down. The different kinds of permissions. There are three main types of permissions. The first one is read, which allows someone to view the contents of a file or list the contents of a directory. Then we have which is W allows someone to modify or delete a file or add or remove files in the directory. Then we have execute, which allows someone to run a file as a program or enter a directory. For example, if you have read permission on a file, you can open and read it. If you have write permission, you can edit or delete it. If you have execute permission, you can run it as a program like a game or application. We have permission groups. File permissions are assigned to three groups. The first one is owner, the person who has created the File or the directory. Then we have group, a set of users who share the same permission, then we have others, anyone else who can access the system. For example, let's say you have a file called report dot DOC. As the owner, you might have the read and write permissions, and you can edit and save the file. Your team, the group might only have read permission so that they can view the file but not change anything in it. Then everyone else might have no permissions at all, so they can't even open the real life example would be, imagine that you are a teacher and you have a folder called grades. As the owner, you have read and write permissions so that you can add and update the grades. Your teaching assistant, the group has the read permission so that they can view the grades but cannot change them. The students have no permission so they can't access the folder this ensures that only the right person can access or modify sensitive information. In conclusion, file systems and disk managements are like organizing and securing your filing cabinets, files and directories help you organize your data. Why file permissions ensure that only authorized users can access or modify it. I hope that this lesson has helped you understand how your computer manages files and permissions. Thank you for joining me in this lesson and I will you in the next one. 29. Networking Fundamentals: Hello, everyone. Today, we are going to dive into the fascinating world of networking fundamentals. Think of networking as a way your computer talks to each other, just like how we use phones or letters to communicate, but instead of words, computer sends data. In this lesson, we'll cover the OSI model, key protocols like TCP, HTTP, and FTP. Introduction to DNS, IP addressing, and routing. So let's get start. The first one is the OSI model. The OSI Open Systems interconnection model is like a blueprint for how computers communicate over a network. It breaks down the crosses into seven layers, each with a specific job. Think of it like building a house. You have a foundation, walls, plumbing, and so on. Each layer of the OSI model builds on the below. So a quick overview of the seven layers. The first one is the physical layer which deals with the actual hardware like cables and switches. Then we have the data link layer, which manages how data is sent over the physical connection. Then we have the network layer which handles addressing and routing of the data. Then we have a transport layer which ensures data is delivered reliably and in order. We have the session layer, which manages the connection between the devices. The presentation layer translates the data into a format that the application can understand. The application layer where applications like web browsers and email clients operate. For example, when you send an email, the application layer is your email presentation layer formats the email into a standard format. The transport layer ensures that the email is delivered correctly. The network layer finds the best paths to the recipient. The physical layer sends the data through cables or WiFi. Each layer works together to make sure that your email reaches its destination. Let's talk about the protocols. A protocol is like a set of rules for how computers communicate, just like how we follow rules when speaking or writing letters, follow protocols to send and receive data. The key protocols are TCP IP, which is transmission control protocol or Internet protocol. The TCP IP is the foundation on the Internet. It's like a postal system for the computers. The IP handles addressing so that the data knows where to go. The TCP ensures that the data is delivered reliably in the correct order. For example, when you send a letter to an address, which is the IP, tell the post office where to deliver it and the envelop, which is the TCP ensures that it arrives intact. The HTTP is used for browsing the web. It's like asking for a book from a library. When you type a website address, your browser sends an HTTP request to the server and the server sends back the web page. For example, when you visit ww dot example dot, your browser uses HTTP to fetch the page and display it to it. Then comes FTP file transfer protocol. The FTP is used for transferring files between the computers. It's like sending a package through a career service. You can upload or download files using FTP. For example, if you want to share a large file with your friend, you might use an FTP server to upload it, and your friend can download it from there. Now let's talk about DNS, which is domain name system. DNS is like a phone book on the Internet. Translates human readable domain names like www.google.com into IP address 142.250.190.14 that your computer can understand. For example, when you type www.google.com on your browser, DNS looks up the IP address for Google's server so that your browser can connect to them. Without DNS, you would have to remember numbers like 142.25 019014 instead of simple names like google.com. Next like to talk about IP address. An IP address is like a home address for your computer on the Internet. It's unique number that identifies your device so that data can be sent to the right place. There are two types of IP address. First one is IPV four, a 32 bit address written as four numbers separated by dots. Example 192.16 eight.one.co. IPv six is a 12 ight eight bit address written in a hexadecimal format. For example, think of your home address. If someone wants to send you a letter, they need your address. Similarly, if a computer wants to send data to another computer, it needs its IP address. Finally, let's talk about routing. Routing is like finding the best path for your data to travel from one computer to another. Routers are devices that help direct data packets to their destiny. For example, imagine you are driving from your home to a friend's house. You might use a map or a GPS to find the best route. Routers do the same thing for data. They look at the destination IP address and decide the best part to sell the data. If one path is busy or broken, the router can choose an alternative path, just like you might take a different route when there is a traffic. Let's tie all those together with a real life example. Imagine you are sending a letter to a friend. The OSI model is like a process of writing, addressing, and mailing the letter. The TCPIP is the postal system that ensures that the letter is delivered correctly. The DNS is looking up your friend's address in a phone book. The IP addressing is like writing your friend's address on the envelope. Routing is like the post office deciding the best way to deliver your letter. This is how computers communicate over the Internet. But instead of letters, they send data packets. In conclusion, networking is like building a communication system for computers. The OSI model protocols, BNS, IP addressing and routing all works together to ensure that data gets to where it needs to go. I hope that this lesson has helped you understand the basics of networking. Thank you for joining me today, and I will see you in the next module. 30. Introduction to Web Development : Hello, everyone. Today we are going to explore the exciting world of web development. Think of the Internet as a giant library and websites as the books in that library. Web development is a process of creating those books. In this lesson, we will cover the basic of web technologies like HTML, CSS, and JavaScript and how web browsers and web servers work together to bring the website to life. Let us dive into the first lesson that is introduction to web development. Web Development relies on three core technologies, HTML, CSS, and JavaScript. Think of them as the building blocks of the website. Let's break them down one by one. HTML is like the skeleton of the website. It provides the structure and the quartet. Imagine that you are building a house. HTML would be the framework like the walls, doors, and vendors. For example, if you're creating a web page with the heading, a paragraph and an image, the HTML, what you see over here, is to define those elements. This is a simple example. H one is the heading. P is the paragraph and I and G is an image. Again, HTML tells the browser what to display, but it doesn't control how it looks. That is where CSS comes in. CSS is like the paint and the decoration of your house. It controls the style and appearance of your website. With CSS, you can change colors, fonts, pacing, and even the layout of the page. For example, let's say that you want to make the heading blue and center it on the pig. You would use CSS, something like this, where the color blue makes the text blue, and the align the text align centered centers the text GSS works hand in hand with the HTML to make the website visually appealing. JavaScript is like the electricity in your house. It adds interactivity and functionality to your website. With JavaScript, you can make things happen when your user clicks a button, fills out forms or interact with the peach. For example, let's say that you want to display out image when someone clicks the button. So you would use JavaScript. Over here, in this example, when the button is clicked, a pop up message saying hello world appears. JavaScript brings websites to life by making them dynamic and interactive. Now that we know the building blocks of a website, let's talk about how websites are delivered to your screen. This involves two key players, which are web browsers and web servers. A web browser is like a librarian who fetches the books for you. It's the software you use to access websites like Chrome, Firefox or Safari. When you type a website address like www.example.com, in your browser, it sends a request to the web server. The browser then takes the HTML, CSS, and JavaScript files it receives and displays them as a webpage. For example, think of your browser as a chef. It takes the ingredients that is HTML, CSS, and JavaScript and combines them to create a delicious dish, which is the webpage. A web server is like a library itself. It stores the website files and delivers them to your browser when they are requested. For example, when you visit our website, your browser sends a request to the server. Saying, Hey, can I find the files for this webpage? The server responds by sending the HTML, CSS, and JavaScript files for the following. Web servers are powerful computers that are always running. So website are available for you 247. They are the backstage crew that make sure everything runs very smoothly. Let us tie this all together with a real life example. Imagine that you're ordering food online. HGML is like the menu. It tells you what's available. CSS is like the design of the menu. It makes it look nice and easy to read. JavaScript is like the waiter. It takes your order and makes things happen like adding items to your the web browser is like the restaurant's front desk. It takes your request and communicates it with the kitchen. The web server is like the kitchen itself. It prepares the food, which is the webpage, and sends it back to the front desk, which is a browser. This is how web technologies, browsers and servers work together to create a website we use every day. So in conclusion, web development is like a building and decorating a. Again, web development is like building and decorating a house. HTML provides the structure, CSS, adds, the style, and JavaScript brings the life to it. Web browsers and servers work together to deliver the website to your screen. I hope that this lesson has helped you understand the basic of web development. Thank you for joining me today, and I will see you in the next lesson. 31. Front-End Development : Welcome to today's lesson of introduction to front end Development. Front end Development is all about creating the part of the website that the user sees and interacts with. Think of it as designing the face of the building. It is what makes the website look good, easy to use, and functional. In this lesson, we will cover three key technologies that power the front end development. That is HTML, the structure of the webpage, the CSS, the style and design of the webpage, and JavaScript, the interactivity and functionality. By the end of the lesson, you will understand how these three work together to create a beautiful and interactive website. HTML looks like the skeleton of the webpage. It provides the basic structure. Without HTML, a webpage wouldn't even exist. HTML uses tags to define elements like headings, paragraphs, and images, and even links. So the key elements of HTML are the heading that is used to create the huge big titles, which is kept under H one tags. A real life example would be think of these headings as the heading of a newspaper that are used to grab your attention. Then comes the paragraphs which are for the text content and are kept under the P tags. Just as you notice, to start the heading tag, we write H one to start the heading tag and to end the heading tag, we writes one. Similarly, for paragraph also, we write P in the tags to start the paragraph and we end it with P. For images that are used to display pictures, we write the image tag, the image tag includes the source from which the image needs to be extracted. And also, in case the source does not work, we also give out an alternate text. You might have seen on your websites sometimes when the image is unable to be processed, you get a text over there, and that is the alternative text. Then we have the links which is used to navigate between the pages, and these are written under the A tags. CSS is like the clothing and the makeup for a web page. It makes a contet look visually appealing by adding colors and fonts and layouts and spacing. The key CSS concepts are selectors and properties. Selectors choose which HTML element to start and properties define how the element should look. For example, this makes all the H one headings blue and sets their font size to 24 pixels. Then we move on to box model. Every element on the web page is a rectangular box. The box model includes the content, which is the text or the image itself, the pad, which is the space inside the border. The borders at is the edge around the element, then you have the margin, which is a space outside the border. For example, over here, we have style the paragraph. The border we have kept it as one pixel, which is going to be solid black color. The border is going to be solid black color with a padding of ten pixel and a margin of 20 pixels. Adding CSS to HTML, there are three different ways that we can do it. Inline CSS, inside the HTML tag itself, we can add the CSS.ide the paragraph itself we have written style is equal to color red. So the text is going to be red. In internal CSS, inside a style tag, we can write it in the head tag. So over here, we have written a style tag where the body is going to have a background color of a light gray. Then we have the external CSS in which we create a separate dot CSS Pi and we link it to the original HTML file using the Link a real life example for this would be think of our restaurant's menu. HTML defines the items on the menu while the CSS makes it look professional with fonts, colors, and layout. So JavaScript is what makes the page interactive. It adds behavior like responding to the user's click, updating content dynamically, and validating the fonts. The key Java concepts are variables which store the information to use it later. So over here, we have saved the name as Alice. So whenever name is called, Alice will be shown on the screen. Then we have the functions. These are the reusable blocks of code, which can be used anytime in the web page. So over here, we have the GET function. Whenever the GET function is called, it is going to pop up a message. Welcome to our website. Then we have the Dom manipulation. The Dom which is document object model represents the HTML structure as an object. It allows the JavaScript to make changes to it. So over here, we have a document dot query selector function. This is basically going to change the color of the first H one tag, which is the heading tag element to green. Then we have events. Actions that happen on a web page like clicks or key presses are events. The JavaScript code over here, as soon as the button is clicked, it is going to generate a alert button click. By using your GML CSS and JavaScript, you will be able to create a visually appealing interactive web page. Now that you understand the basics, practice by building a simple web page for your favorite hobby, experimenting with CSS animations and adding more interactivity with the JavaScript like a simple to do list. Front line development is a creative and rewarding skill that brings ideas to life key by experimenting and key learning. Thank you for joining me, and I will see you in the next lesson. 32. Back-End Development : Welcome to today's lesson to introduction to Bend development. Backend development focuses on the server side of web application. That's what happens behind the scenes to make a website or app functional. When the front end is what the users see and interact with, the backend powers it by storing and managing data, hunting requests from the front end, and performing calculations or log imagine that you are ordering fruit through a delivery app. The backend is responsible for storing restaurant menus in the database, calculating the delivery fee, processing your payment, and updating the order status in rear die. In this lesson, we will explore backend technologies like Node JS, Dijango and Flask. Backend technologies are tools and frameworks that help developers create the server side applications. Let us look at the popular ones. The first one is no JS. NodeJS is a run d that allows you to run JavaScript on the server. It is fast and great for building scalable web application. The key features of NodeJS are that it can handle many requests at the same time, making it perfect for apps like chat platforms and live streaming services. The real life example for node JS would be when you want to send a message on Whatsapp, NodeJS could be used to instantly process and deliver your message. Then FM Django. Python based web framework that helps developers build secure and robust applications very quickly. It comes with a built in tool for authentication, database handling, and admin tannins. A real life example for Django would be a blog platform, Light Wordpress could be built with Django to manage posts and user accounts. Then comes FAS, another Python based web framework, but lighter and more flexible than Jang. It is minimalist and lets you choose the tools that you want to use. Real life example for this would be FAS is great for building small apps like weather tracker that show data from an external API. So what is an API? Application programming interface. An API is a way for applications to communicate with each other. Think of it as the waiter in a restaurant. The waiter, which is the API, takes your order, which is the request to the kitchen, which is the server. It brings back your food, which is the response. There are few defined steps to build an API. The first one is define the endpoints. Decide what requests the API will handle. For example, the user will fetch the list of users, orders will create new orders. The next one is to use a proper framework. Frameworks like Express for node JS or FAS that make easier to create ABIs need to be decided. The third one is send responses. APIs respond with data usually in a JCN format, which is easy for computers and humans to taking a real life example over here, when you search for a movie on Netflix, the front end sends a request to an API which fetches the list of matching movies from the server, working with databases. What is a database? A database is where an application stores its data. Think of it as a digital filing cabinet. The back end retrieves and updates the data as needed. Are different types of databases. The first one is relational database, which is also EQUAL. Data is stored in tables with rows and columns like a spreadsheet. The examples for this will be MySQL and post RC SQuL. A real life example would be a library system storing books, and it has their borrowers. Then comes no relational databases, which is also known as noSQuL. Data is stored in a flexible format like a JCNFle. Example would be Mongo TP. A real life example would be a social media app storing users profiles and their posts, their comments, et cetera. How do we work with databases? The first step is to connect to the database using library or the OL, which is object relational mapping too. We connect to the database. Then we perform crude operations where C is for create, which is add a new data, then we read, we retrieve the data, then we update, we modify existing data, or we can also delete, which is removing the data. Realized example over here would be when you log into your bank app, the Ben retrieves your account information from the database. Now that we have learned the basics of Ben development, you know that it is a backbone of web applications. With practice, you will master the art of creating reliable, scalable and secure systems. Thank you for joining me in today's lesson, and I will see you in the next one. 33. Full-Stack Development: Welcome to the lesson on introduction to Full Sag development. What is a full S development? Full stag development means that working on both front end and back end. So what the user sees and the functionality that goes behind the scene. As a full Stag developer, you create entire application that handles everything from desiring the user interface to managing the data on the server. Imagine that you are building an e commerce website. The front end handles the display products, the shopping cart, and the checkout page. The backend processes orders stored customers data and communication with a payment gateway. As a full stack developer, you will integrate both parts to ensure that the website works seriously. How does front end and Bend work together? The front end interacts with the user while the backend processes and stores data. Communication between the two happens through APIs, which is application programming interface. Steps that are required to integrate the front end and back end are first to build the front end, create the user interface using the HTML, CSS, and JavaScript. Use front end frameworks like react, angular or Vets or dynamic and interactive features. Second would be to develop the back end. Set up EPIs using frameworks like SAS, Django, or Express, connect the back end to a database to store and retrieve the data. The third would be to connect the front end to the back end. Use HTTP requests like get pose, put D to send and receive the data between the front end and the backend. Front end sends a request to the back end API to fetch a list of products. The back end retrieves the data from the database and sends it to the front end. Imagine when you log into your email, the front end collects your username and password. The data is sent to the back end which verifies it. If it is correct, the back end sends your inbox data to the front end for display. Front end frameworks help developers to create dynamic and interactive user interfaces more efficiently. The three popular frameworks are a which is a JavaScript library for building user interfaces. Its key features are that it is component based architecture. Reusable pieces of user interface like buttons or forms are built as components. A real life example would be Facebook that uses react to build features like the news feets and notification. Angular, which is a full fledged framework maintained by Google for building dynamic web pages. The key feature of angular is that it is a two way data binding which keeps the UI and the data synchronized automatically. A real life example would be the GMs interface, which is built using the angular to manage complex interactions and dynamic updates. The third one is w dot js, a lightweight framework for building interactive web application. Key features for view is that it is simple to learn and integrate easily with other projects. A real life example would be that many startups use views for its simplicity and fast performance, such as Alipaba's web platform. Why do we use the front end frameworks? We use the front end frameworks because it is faster development with pre built tools and features. It helps in better organization of codes using components. It gives us an enhanced user experience with smoother interactions. Full stale development is a highly in demand skill. With practice, you can bring an entire application to life and handle every part of their creation. Thank you for joining me in today's lesson, and I will see you in the next one. 34. Basics of Machine Learning : Welcome to the first lesson in our series on machine learning and artificial intelligence. In this lesson, we will break down the basics of machine learning in simpler terms. Our focus will be on understanding what machine learning is, its types, and some commonly used algorithms. So let us dive in. The first lesson that we are going to cover is basics of machine learning. What is machine learning? Machine learning is a field of computer science where computers are trained to learn from data and improve their performance over time without being explicitly programmed. Think of it as teaching a machine how to make decisions or predictions based on pattern in the data that it sees. A real life example would be. Imagine that you are teaching a virtual assistant like Alexa or Siri to recognize your voice. Instead of programming it with specific rules for every possible way, you might say hello, you provide it with examples of your voice saying hello in different tones, accents and also speed. Over the time the assistant learns to recognize your voice more accurately, types of machine learning. There are three main types of machine learning, supervised learning, unsupervised learning, and reinforcement learning. Let's break these down with simple definitions and examples. The first one is supervised learning. In supervised learning, the machines learn from label to data. This means that the data on which the machine is trained already has all the correct answers. Example, predicting house prices. You provide the machine with the data about the houses, such as the number of bedrooms, square foot and whether they have garden or not along with the actual prices of those house. The machine learns about the relationship between these features and the so it can predict the price of a new house based on these features. Next is unsupervised learning. In unsupervised learning, the machine works with data that has no labels. It tries to find patterns or groupings on its own. Example, grouping customers in a store based on their buying habits. The machine might find that one group often buys baby products while another group prefers fitness items. This helps business target specific groups with specific advertisements. The third one is reinforcement learning. Reinforcement learning, the machine learns by trial and error method receiving rewards and penalities for its actions. Example, teaching a robot to play a video game. The robot tries different actions and when it wins points, it learns that those actions are good, but when it loses the points, it avoid those action in the future. Some common machine learning algorithm. Now that we have covered the types of machine learning algorithms, let us expe some basic algorithms. Don't worry, we keep it big enough friendly with oic examples. The first one is linear regression. Linear regression is a method used to predict the values based on the relationship between the variables. It assumes there is a straight line relationship between the input, which is a independent variable and the output, which is the dependent example would be predicting your monthly electricity bill. Let's say your electricity bill depends on how many hours you use your air conditioner. By analyzing the past bills and usage hours, linear regression can estimate your next bill based on the hours you plan to use the AC this month. Another one is decision. A decision tree is like a flow chart that splits the data into branches to make decisions. It asks a series of yes no questions to arrive to a conclusion. Example, deciding whether to buy a car or tree might start by asking, is the price within your budget or not? If yes, it moves to the next question. Does it have good fuel efficiency? It keeps asking questions until you reach your final decision. The next one is K nearest neighbor or KNN algorithm. The KNN method is used for classification or prediction. It works by finding the data points closest to the one being analyzed and making a decision based on the majority. For example, recommending movies. If you like a particular movie, KNN finds people who like the same movie and recommends other movies they enjoy. Example, if you like the Avengers, KNN might suggest superhero movie. A quick recap, machine learning is about teaching computers to learn from data without being explicitly programmed. There are three main types of machine learning, supervised, unsupervised, and reinforcement learning. Common algorithms include linear regression, which is used for predicting continuous values, decision trees, which is like a flow chart decision maker, and K nearest nibbur which uses neighbors for classification or prediction. That's it for lesson one. I hope that this introduction has helped you understand the basics of machine learning. Thank you for joining me in today's lesson, and I will see you in the next one. 35. Data Preprocessing and Feature Engineering: Hello and welcome to this lesson on data preprocessing and feature engineering. Before we can build amazing models using machine learning, we need to prepare our data. Think of it like cooking. You can't make a great meal without prepping your ingredients. In this lesson, we are going to learn how to clean, transform, and enhance our data so that our models perform their best. Let us first understand how data training and transforming happens. What is data Clening? Imagine you're organizing a party and someone gives you a guest list with messy handwriting, wrong phone numbers and duplicate names. Before you send invites, you need to fix that, right? That's exactly what we do with data Clei. The key tasks of data cleaning include handling missing values. Some data might be missing. For example, a survey might have blank answers for certain questions. How to fix it, you can fill the missing values with default values like the average age if the person's age is missing, or you can remove the row entirely if it is incomplete and you cannot fill anything over there. Next, in data cleaning, we remove the duplicates. Imagine having the same person on your guest list twice. In data, duplicates can create confusion, so we remove. Next in data cleaning is correcting errors. Example, a dataset has someone's height as 1,500 centimeter instead of 150 centimeter, ting it involves fixing such error. Then we have data transformation. Data transformation is about changing the data into a format that's easier to work with. Think of it as organizing your guest list alphabetically or by the table arrangement. For example, converting text to numbers. Machines love numbers, not words. For example, if you have a column for colors with values like red, blue, green, you can convert them into numbers like for red, you can have one. For blue, you can have two, and for green, you can have three. Also, transforming males and female as zero and one for genders. A transformation also includes categorical encoding. For complex categories, we use a technique called one hot encoding, which is nothing but creating separate columns for each category. Now let us introduce you to scaling and normalization. What is scaling? Imagine comparing the height of trees in meters and their ages in years. The numbers are in completely different ranges, 20 meters versus 200 years. Scaling adjusts the number so that they are within similar ranges. The two common techniques of scaling are standardization and minmax scaling. In standardization, what we do is we center the data around zero and scales it based on the standard deviation. For example, S scores from different exams can be standardized to compare the performance of any student. The MinMax scaling, we rescale the value to a range of zero to one. If house prices in a dataset range between 100 k to 1 million, scaling would transform the hundred k to zero and 1 million to. Then we have normalization. Normalization ensures that the data values are proportionate. Think of it as adjusting all the ingredients in a recipe to the same proportion no matter how much food that you are cooking. For example, a dataset with values like 5,505,000 can be normalized to simpler ratios like 0.05, 0.5 and five. This is useful in algorithms like K nearest neighbors that depends on the distances. Now let us introduce you to feature selection. What is feature selection? Not all data is equally important. Feature selection is like packing for a trip. You only take what's necessary. Why do we need feature selection, irrelevant or redundant features can confuse our models. Also, using fewer features also, using fewer features can save time and resources while we are running our models. How do we select features? There are multiple methods. We can perform co relation analysis where we can check if one feature is closely related to another. For example, weight in pounds and weight in kilograms are redundant. We can also select feature importance from models. Decision trees and random forests can tell us which features are the most useful ones. For example, imagine that you're building a model to predict a student's exam score. Features like study hours and class attendance might be important, but Tabrit color is not important, so we will not select favorite color. In conclusion, data pre processing and feature engineering are the unsung heroes of machine learning. Tuning, scaling, normalizing, and selecting the right features are essential steps to ensure that your model works efficiently and accurately. Remember, good data preparation leads to great results. Thank you for joining me in today's lesson, and I will see you in the next one. 36. Deep Learning Fundamentals: Welcome to an exciting world of deep learning. Today we are diving into the fascinating field that powers technologies like voice assistant, face recognition, self driving cars, and even tools that generate art. This lesson will introduce you to the basics of neural networks, the foundation of deep learning, and the tools like tensor flow and Pitch. What is deep learning? Deep learning is the subset of machine learning inspired by the way that human brain work. It uses layers of connected neurons to process and learn from data. For example, think of how we recognize faces. We don't just see a phase as one thing. We first notice the features like eyes, nose, mouth, and then combine these to understand that it is a phase. Deep learning works similarly by identifying patterns in steps. Now, let us study a little bit about neural networks. Neural networks are the core of deep learning. Let us break them down. How a neural network works. The first thing that a neural network has is a neuron or a node, the basic unit of a neural network like a single brain cell. Then it has layers. It has a input layer, which takes the raw data, example, an image or a centre. Then it has hidden layers, which process the data and find patterns in it. Then we have the output layer which produces the final result. For example, this is a cat take a real life example over here. Imagine teaching a robot how to recognize apples. The input layer would be the robo looks at an image of an apple, which are pixels. The hidden layer detects the shape, texture, and colors. The output layer, it confuses that it is an apple. The key concepts of neural network. First one is weights and biases. These are the numbers the network adjusts to learn. For example, imagine breaking cookies. The recipe, which is the weights changes depending on your taste, the model that it is lo then we have the activation functions. They decide if a neuron should activate or stay off. For example, think of it like a light switch. If the input is strong enough, the light, which is the neuron turns on or else it stays off. Then we have the loss function. This measures how far off the network's predictions are. For example, if a robot mislabels an orange as an apple, the loss function will indicate how wrong it is so the network can improve in the future. Deep learning versus traditional machine learning. In traditional machine learning, you often tell the model which features to focus on. In deep learning, however, the model learns those features automatically. For example, machine learning, you tell the model to look for round shapes and red colors to identify apples. In deep learning, the model figures out these patterns on its own. To build deep learning models, you need tools. Two of the most popular frameworks are tensor flow and Pytoch. So what is tensor flow? TensorFlow is a library developed by Google that makes building and training deep learning models so easy. It's like having a toolbox for creating AI systems. Why use tensor flow? It is powerful and widely used in industries like healthcare, finance, and tech, and also it supports both Bigler and advanced level users. For example, Google Photos uses tensor flow to recognize faces and sort your pictures automatic what is Pitoch? Pitoch is developed by Facebook. It is another deep learning library. It's known for being the most beginner Peny and great for research purposes. Why do we use Pitoch? It is flexible and easy to debug, and also researchers love it for experimenting with cutting edge AI models. For example, Pytoch is used in self driving car researches to help models process image from cameras and sensors, tensor flow versus Pytoch which is better. Both framework do similar things, but here's a simple comparison. TensorFlow is great for production used in companies, while Pitch is great for research and experimentation. Think of it like choosing between two cars. One might be better for a long trip like transoflow while the other is more fun for short drives and testing new routes, which is the Pytoch. So concluding over here, deep learning is a game changer in technology, and neural networks are its heart. By understanding how they work and using tools like TensorFlow and Pitch, you will be able to create models that solve real world problems. Thank you for joining me today, and I will see you in the next lesson. 37. AI in Computer Science: Welcome to this exciting lesson on AI and computer science. Artificial intelligence, which is AI is transforming every corner of the tech world from apps that talk to you like C or Alexa to the systems that analyze photos or drive cars. In this lesson, we'll explore some of the key AI algorithms, their applications, and two essential areas of AI, NLP, which is natural language processing and computer vision. Let's get started. First, we'll take a look at AI algorithms and their application. AI algorithms are like a brain behind the intelligent system. They allow computers to solve problems, learn patterns, and make decisions. Let us look at some points. Let us look at some important algorithms and how they are used in real life. The first one is decision tree. What is it? A decision tree is like a flow chart where decisions are step by step, based on the conditions. Again, a decision tree is like a flow chart where the decisions are made step by step, based on the condition. For example, online shopping websites use Decision Tree to recommend products. If you buy a phone, the website if you buy a phone, the website might suggest phone cases or headphones based on your purchase history. The next one is Neural Networks. What is Neural Neural networks mimic how human brains work by processing data through layers of neurons. For example, Spotify uses neural network to recommend songs by analyzing your listening habits and comparing them to others. Then we have cluster algorithms. What are thruster algorithms? Tuster algorithms, group similar data coins together. For example, Google Photos. Google Photos use clustering to automatically group photos of the same person or the same location. Then we have reinforcement learning. What is reinforcement learning? Reinforcement learning is like training a the system learns through rewards and punishments for its actions. For example, self driving cars, self driving cars use reinforcement learning to figure out how to stay in the correct lane, avoid obstacles, and safely navigate or traffic. Let's move on to natural language processing, which is NLP. What is NLP? NLP is a branch of AI that focuses on enabling computers to understand, interpret, and respond to human languages, whether it's text or speech. How does NLP work? NLP breaks down the language into smaller parts to process it. The first part being tokenization, splitting sentences into words or phrases. Example, breaking I love AI into I love and AI, then understanding its feeling, identifying the context of the word, example, understanding that that means an animal in one sentence and a sport equipment in another sentence. Then generating response creating replies or suggestions based on the input. For example, chat boods like Char GPT or CD. Some real life applications of NLP are chat boards where the customer service boards on websites use NLP to answer the questions. Voice assistants like Alexa CE, Google Assistant, use LLP to understand commands like pay my favorite song. Then language translation tools like Google Transit, convert text from one language to another using NL. Let's move on to computer vision. What is computer vision? Computer vision is a field of AI that helps machines see and integrate the visual. It involves analyzing images or videos to identify objects, patterns, or actions. How does computer vision works? Computer vision works on image processing where the computer analyzes pixels in an image to detect the edges, colors and textures. It also works on object detection. It identifies what is in the image, example, a cat, a car, a human face, et cetera. Also pattern recognition, recognizing specific objects, gestures or even emotions. Real life applications of computer visions are facial recognitions, unlocking phones using face ID or tagging friends on Facebook, medical imaging where they have been detecting diseases like cancers from Xrays and MRIs, self driving cards, recognizing road signs, pedestrians, and other vehicles, also augmented reality where apps like Snapchat use computer vision to overlay fun filters on human faces. Then we have AI in real life scenarios. Connect everything we have discussed, let's explore some exciting real world AI examples. The first one being NLP plus computer vision. Google Lens combines computer vision and NLP, where you can take a picture of a restaurant menu and it translates it into another language in real time. Then AI for accessibility, apps like CAI, use computer vision to describe objects, peoples, or text to visually impaired users. Then in healthcare, AI chat boards with NLP help patients by answering health related questions while computer vision detects abnormalities in ethical scats. So concluding it over here, AI is revolutionizing the computer science by enabling machines to think, see, and communicate like humans with algorithms like decision tree, neural networks and cluster algorithms, alongside with technologies like NLP and computer vision, AI powers the tools and applications we use every day. So that's it for today. Think about how AI is already part of your daily life and how you might use it to solve problems in your own projects. So thank you for joining me today, and I will see you in the next module. 38. Introduction to Cybersecurity : Come to the module of cybersecurity fundamentals. Let us start the lesson or introduction to cybersecurity. In today's digital world where almost everything from shopping to banking to social interaction happens online, cybersecurity is more important than ever. In this lesson, we discover what cybersecurity is, why it matters, and learn about some common threats like virus, Mlbare, phishing, and hacking. Let's get started. What is cybersecurity? Cybersecurity is a practice of protecting computers and networks. Cybersecurity is a practice of protecting computers, networks and data from unauthorized access attacks and damages. Think of it as a digital shield that keeps hackers and other online threats from stealing sensitive information or disrupting the system. Why is cybersecurity so important? Imagine leaving your house unlocked with valuable possessions inside. That's what it's like for a system without cybersecurity. In a world where businesses and individuals rely heavily on technology, protecting digital assets is very critical. For example, some real life examples is personal safety. If your email gets had, someone could access your sensitive conversations or bank accounts. Also, business impact. In 2070, the Wanna cry ransomware attack shut down hospitals posing them to delay surgeries and patient care. It was a wake up call for the importance of cybersecurity in critical industry. Some common threats in cybersecurity. Now that we understand what cybersecurity is, let us explore some common threats. These are the bad guys trying to get into your digital house. The first one is virus. What virus? A computer virus is a malicious software that can infect your system, spread to other files, and cause damage. Just like a biological virus, it needs a host, which is your computer to replicate. How does it work? A virus can come from downloading files, opening suspicious email attachments or visiting unsafe website. For example, the I LU virus in 2000 spread through emails, disguising itself as a love letter. It deleted piles and caused billions of dollars in damage worldwide. The next is Malware. What is Malware? Malware stands for malicious software. It's a broad term that includes virus, spyware, ransomware, and much more. It's designed to harm, exploit and steal data. What are the different kinds of malware? There is Spyware, which secretly monitors your activities like tracking your passwords, et cetera. Then comes ransomware, which locks your files and demands payment to unlock them. For example, in 2021, the colonial pipeline ransomware attack caused fuel shortage in US because hackers locked the company's system and demanded payment. Then comes phishing. What is phishing? Phishing is when the attackers trick you into revealing sensitive information like passwords, credit card numbers by pretending to be trusted source. How does it work? Might get an email that looks like that it's from your bank asking you to confirm your bank details, but the link leads to a fake website designed to steal this information. Real life example for fishing was when millions of people fell for the PayPal phishing scam. They were the fake emails that direct users to fraudulent websites to steal gan credits. How to spot phishing attempts. Look for typos or poor grammar in the email. Be cautious of links or attachments from unknown sources. Then comes hacking. Hacking is the act of breaking into computer systems or network to steal, alter or destroy information. Not all hacking is bad. Some are ethical hacking, called as white hat hackers that help organization improve their security. There are different types of hackers. The first one is black hat hacker, or malicious hacker who steal or damage the data. Then there are white hat hackers, which are ethical hackers who work to protect the system. Then there are gray hat hackers, which are a mix of both. They don't have malicious intent, but they may break the law. Real life example would be in 2016, Yahoo was hacked and 3 billion user accounts were compromised, exposing personal information like names, emails, and even passwords, how to stay safe online. There are some tips that you can follow to save yourself from these threats. The first one is use strong password. Create a complex password and never reuse them across account. Then install antivirus softwares. A good antivirus software can detect and block viruses and also malwares. Be careful of the links and attachments. Don't click on suspicious links or open unexpected email attachments. Then enable two factor authentification also known as two effect. This adds an extra layer of security to your account. In conclusion, cybersecurity is not just for the tech experts. It is something everyone should take care about. But understanding threats like virus, malware, phishing, and hacking, you are taking the first step in protecting yourself and your data. Thank you for joining me in today's lesson, and I will see you in the next one. 39. Cryptography Basics: Welcome to this lesson on cryptography basics. Cryptography is like a secret language of the Internet. It keeps our data safe from prying eyes. Every time you send a message, shop online, or even log into an account, cryptography works behind the scenes to protect your information. Today, we'll cover the difference between symmetric and asymmetric encryption and learn about public key infrastructure, PKIs and SSL TSL, which are the essentials to secure the information. So let's dive in. First, let us start with the symmetric and asymmetric encryptions. So what is encryption? Encryption is a process of converting the readable data which is plain text into an unreadable format, which is the cipher text so that only authorized people can access it. Think of it like locking a message in a box where only the person with the key can open it. Then comes symmetric encryption. What is symmetric encryption? In symmetric encryption, the same key is used to both encrypt and decrypt the data. Simple and fast, but it has the challenge of securely sharing the key. For example, imagine you and a friend share a die with a lock. Both of you have the same key to open it. But if someone else gets the key, they can also read your diary. In technology, symmetric encryption is used in file storage and also data backups. An example algorithm that is used is AES, which is advanced encryption standards used in apps like WhatsApp to encrypt the messages. The asymmetric encryption uses two keys. The first is a public key that is shared openly and used to encrypt the data. Then we have the private key which is kept secret and used to decrypt the data. This way, even if the public key is shared widely, only private key can unrop the information. Imagine a mailbox. Everyone knows the location, which is the public key and can drop letters in it, which is the encrypted data, but only you have the key to open the mailbox, which is a private key and read the letters. Asymmetric encryption is used in email encryptions and secure websites. An example algorithm that is used in asymmetric encryption is the RSA algorithm, which is commonly used to secure web browsing. Comparing the symmetric and asymmetric encryptions, the key that is used in symmetric, there is only one key for encryption and decryption. For asymmetric encryption, there is a publish and a private key, and it is a pair. The speed for symmetric encryption is faster, and for as encryption, it is much slower. A use case is that for symmetric encryption, it secures file storage while asymmetric encryption secures communication over the Internet. Let us study a little bit about the PKI, which is public key infrastructure. So what is public key infrastructure? It is a system that manages public and private keys to enable secure communication. It is the backbone to secure the Internet transactions. How PKI works, the first step is a certificate authority, which is the CA, a trusted organization like DigiCt or Let's encrypt issues a digital certificate which proves the authentity of the public key. Think of it as like a driver's license that proves your identity. Next, we have digital certificates. These contain the public key and the information about the owner. When you visit a website, the certificate confirms that you are communicating with the right server. Example, when you shop online, PKI ensures that your credit card details are encrypted and sent securely to the retailer server, not a hacker pretending to be a retailer. Next, we have the SSL and TSL, which is secure sockets layer or transport layer security. SSL and its modern version, TSL is cryptographic protocol that secures communication over the Internet. It's why that you see a log icon on your browser's address bar when you are visiting a secure website. How does SSL and TSL work? When you visit a secure website, for example, htp example.com, your browser asks the server for its digital certificate. The server sends its digital certificate which is issued by a trusted CA, and then your browser verifies the certificate. If it's a valid one, the browser and the server establish a secure connection using the encryption. You log into your online banking account or make a payment on Amazon, the SSL or TLS ensure that no one can intercept or steer your sensitive data like passwords or credit card numbers. While SSL or TLS is important, it prevents eavesdropping. Hackers can't intercept data between your browser and the website. Authenticates the website. It ensures you're communicating with a legitimate website and not an imposter. Also, it encrypts the data. Your information stays private during the transmission. Have you ever noted that in SDTPS in your browser, the S stands for secure, and it indicates that SSL and TLS is active over here. In conclusion, cryptography is essential for keeping our digital world secure, whether it's symmetric encryption for a faster data protection or asymmetric encryption for secure communication. These techniques are the foundation of cyberse with systems like PKI and protocols like SSL, MPLS, we can safely browse, shop, and communicate online. Thank you for joining me in today's lesson, and I will see you in the next one. 40. Network Security: Welcome to this lesson on network security. Every time you connect to the Internet, whether it's browsing or using apps or even streaming, your data passes through a network. Network security ensures that this data stays safe from hackers, unauthorized access, and also threats. Today we'll cover essential tools like firewall, intrusion detection system, and VPN, as well as how to secure web applications and databases. So let's get started. Let us start with firewall IDS at VPM. What is firewall? A firewall acts like a security guard for your network. It monitors and controls the incoming and outgoing traffic based on a set of rules. If the traffic looks suspicious, the firewall blocks it. Imagine a nightclub bouncer checking the IDs at the door. Only people on the guest list are allowed in, and anyone suspicious is stopped over there at how does a firewall work? Firewall can filter traffic by IP address, protocols or specific keyword. For example, if a hacker tries to send malicious files to your system, the firewall will block them there itself. What are the different kinds of firewall? There are hardware firewalls. Those are the physical devices that are used in organizations to secure large networks. And then there are software firewalls which are installed on personal computers or devices to protect individual users. The real life application of these are your home router likely has a built in firewall that protects your devices from unwanted Internet traffic. We have the intrusion detection system. What is that? An intrusion detection system, IDS is like a security alarm for your network. It monitors all activity and alerts you if something suspicious happens. How does IDS work? The IDS looks for unusual patterns such as multiple failed login attempts or sudden spike of traffic, which could indicate a hacker trying to break. Other different kinds of ideas, there is signature based ideas. I detects known attack patterns like virus or malware signatures. Then there is anomaly based IDS which detects unusual activity even if it's not a known threat. For example, if someone tries to hack into an organization's email server, the idea sends an alert so administrator can take action quickly. Then we have virtual private network, which is VPN. What is VPN? A VPN, which is virtual private network creates a secure connection between your device and the Internet. It protects your data from being intercepted by the hackers. How does VPN work? VPN creates a tunnel for you in the Internet traffic encrypting it so that no one can see what you're doing online. A real life analogy over here would be imagine sending a letter in a sealed envelope instead of a transparent plastic envelope ensures that no one can read your letter while it is being delivered. The benefits of VPN R, that it gives you privacy. It hides your IP address and browsing activity from the Internet server provider or the hackers. Also, it gives you access. It bypasses the geographic restrictions like watching Netflix shows available only in another countries. Then it also gives you security. It protects your data when using public Wi Fi like cafes or airport. Next, let's move on to securing web applications and databases. Web applications are the software programs that run on the Internet like online banking platforms or e commerce websites. Because they handle sensitive data, they are the prime suspect for the cyber attacks. There are some common threads to the web application like cual injection. Hackers inject malicious code into the web application to access or manipulate the database. Example, a hacker might gain access to the user names and passwords stored in an online stores database. Also, also pus site scripting. A hacker tricks users into entering a login details on a fake login form. What are the best practices that are used for securing web application? The first one is input validation. Ensure that the user input like forms is sanitized to block the harmful codes. Use HTTPS, to cure the connection between the user and your website using SSL and TLS encryption. Also do regular updates. Keep your software and plug ins up to date to fix vulnerabilities. Companies like Amazon and PayPal invest heavily in web applications, security to protect customer transactions and also its data. Well we have securing databases, database stored valuable information like customer details, passwords, and financial records. Protecting these databases is critical for any organization. The common threats to database are data breaches, where the hacker steals sensitive information stored in the database. For example, in 2019, Capital one Data Breach was done in which the personal data of 100 million people was exposed. Then there are ransomware attacks where the hacker encrypts the database files and demand for payments to unlock. Best practices for securing the database are to encrypt sensitive data. Even if the hacker gains access encrypted data will be unreadable, then there is access control, which limits who can access the database. Use role based permissions to ensure only authorized users can make any changes if required. Then there is backup data. For example, online platforms like Instagram secure their database to ensure user photos and personal details remain private and safe. Concluding over here, Network security is like the backbone of safe Internet communication. Tools like firewall IDs and VPN help protect the network and data from external threats. At the same time, securing web applications and database ensure sensitive information remain private. By understanding these fundamental concepts, you are taking big steps towards protecting yourself and others in this digital world. Thank you for joining me today, and I will see you in the next module 41. Capstone Project Planning: Welcome to the final module. In this module, you will understand how to create your first Capstone project. This is where you will take everything that you have learned in this course and put it into practice by building something for rear. In this lesson, we will focus on planning your project. Good planning sets the foundation of a successful project. We will cover the two key topics. The first one is how to choose a project and then how to gather the requirements and set your goals to complete that project. By the end of this lesson, you will have a clear roadmap for your project. So let's get started. Choosing the project is the first and the most important step. Your project should be realistic, aligned with your interests and help you showcase your skills. Here are three more common types of projects that you can choose from. The first one is web application, a website that allows user to interact like an online store or a todo list app. Example, a task management app where the users can create, update, and delete the tasks. Next, you can also build a machine learning model. You can build a model that solves a problem using data like predicting sales or recognizing image. Example, a movie recommendation system based on the user's preference. Then you can also design a software tool, a stand program that solves a specific problem like a calculator or a file organizer. For example, a desktop tool that organizes files into a folder based on the file type. How to choose the right project. Start with your interests. Think about what excites you. Do you enjoy working with data? Are you passionate about design then consider your skills. Choose a project that uses the skills you have learned in this course. For example, if you love coding and logic, try software tool, or if you enjoy data analysis, build a machine learning model, set a realistic score. Don't aim for a massive project like creating the Lex Facebook. Instead, focus on something small but meaningful. For example, let's say that you are interested in fitness. A simple project could be a web app that tracks your workouts and show progress over time. Now perform a brainstorm idea session. Take 5 minutes to think of project ideas. Ask yourself these three questions. What problem do I want to solve? Who will use my project, and what technologies will I use? Write down your two to three ideas and then work on it. Once you have chosen your project, the next step is to figure out what it needs to do and what success looks like. Requirements are the features your projects must have to solve the problem or meet the user needs. Think of them as a checklist. Follow these steps to gather the requirements. The first one is define the problem. Ask, what problem is my project solving? For example, if you're building a todo list app, the problem could be people need a simple way to track their task. Next, understand your users. Who will use your project? What do they need? For example, a workout tracker user might need a way to log the workouts. Also a dashboard to view their progress. Next, list the key features. Break the project into specific features. For example, a movie recommendation system features could be a form to input the favorite genres, a list of recommended movies. Activity to perform over here for gathering requirements would be to get answer to these following questions. What problem am I solving? Who are my users, and what features will my project have? Next, move on to setting goals. Goals help you measure the success and stay on track. They can be broken into short term goals and long term goals. Short term goals have smaller tasks that you can complete Quichi long term goals are the overall objective of your project. For example, short term goals can be to design a homepage for the web app or train a machine learning model on simple data. The long term goals can be to launch the web app for friends and family use and also achieve 80% accuracy with your machine learning models. Make sure that your goals are SMA. S for specific. Clearly define what you want to achieve. Make it measurable. You should be able to track your progress, make it achievable, set realistic goals within your skills and time. It should be relevant. Focus on goals that align with your project idea and also time bound, set a deadline for completing each goal. For example, if you're building a recipe recommendation app, short term goals will be to create a form where the users can input the ingredients that they have. Next, write code to match the ingredients to recipes in your database. Long term goals can be to launch the app and test it with ten users. Conclusion, in this lesson, we covered the two most important steps for planning your Capstone project. Choosing the project was the first one. Pick something realistic, aligned with your interest and achievable with your skill set. Next, gathering requirements and setting goals to identify what your project needs to do and set clear and actionable goals. Take time today to brainstorm and write down your project ideas, requirements, and goals. Good luck, and I can't wait to see your amazing projects come to life. I'll see you in the next lesson. 42. Development and Implementation: Come back to your Capstone project journey. Now that you have planned your project, it's time to build it. In this lesson, we'll focus on how to develop and implement your project by applying the skills that you have learned throughout this course. We will cover applying skills from this course, also using version control and collaboration tools and also testing your project. By the, you will have an understanding of how to organize and execute your project effectively. Let us dive in. Building your Capstone project is the perfect opportunity to showcase everything that you have learned. The first thing that we do is breaking down the development process. For that, you will require to follow these steps. The first one being design your project structure. Think of how your project will be organized. For example, if you are building a Web App, you might have folders for HTML, CSS, and JavaScript files. For a machine learning model, you will need a script for loading data and a model training script and a result visualization script. Next, use algorithms and concepts that you have learned. If your project involves searching and sorting, apply those algorithms over here. Example, a To Do list app could use a sorting algorithm to prioritize tasks by deadlight. Next, write a reusable code. Keep your code tin and modular by using functions. For instance, in a fitness tracker app, write a function that calculate total calories burned for a week. Let's say that you are building a recipe recommendation system. Here's how you will apply your skill. The first one being data handling. Use the data pre processing skills that you have learned to clean your recipe dataset. Next, algorithms. Implement the recommendation algorithm to suggest the recipes based on ingredients a user has. Thirdly, front end basics, design a simple web page for users to input the ingredient. A small activity that I would recommend over here is to start quoting for one small feature of your project today. It could be something like creating a homepage, setting up your dataset, or implementing a simple function. Next, we move to user version control and collaboration tool. What is version control? Version Control is a system that helps you track changes to your code. Collaborate with others and also avoid losing the progress. The most popular tool for this is Git. Why use Git? The first one is that it tracks changes. Git saves every version of your project so that you can go back if something breaks. For example, if you accidentally delete a file or make a mistake, you can restore the previous version. Next just collaborate. Git makes it easy to work with others by allowing everyone to contribute their code to the same project. Third is branching. Branches let you experiment without affecting the made project. Example, you can test a new feature on a separate branch before merging it to the main one. How to use Git. This is really a basic for Big Nows. The first one is initialize a Git repository using the Git in it. This sets the Git to track your project. Next, add and commit to changes. To add files to Git, use Git add, and to save changes with a message you can write Git commit, hyphen and write your message. Over here, we have written, added to homepage. The third one is push to Git. GitHub is a popular platform for hosting your code. Push your projects to GitHub so that you can access it from anywhere. Let's move on to collaboration tools. If you're working with a team, tools like Github and Trello can help. On Github, you can share your code and review contributions from your teammates. On Trello, you can manage tasks and deadlines visually by creating bots for each step of your project. For example, imagine you and a friend are building a weather app together. While you are designing the interface, your friend is writing the code to fetch weather data. Using Git, you can both work on the project simultaneously without overwriting each other's work. Next step is testing your project. Why is testing so important? Testing ensures that your project works as expected and helps you catch errors early on. A bug free project is essential for successful capstone. The different kinds of testing. The first one is unit testing. Test individual functions or components of your project. For example, if your project calculates the BMI, which is body mass index, test the function with different inputs to ensure that it returns correct results. Next is integration testing. Test how different parts of your project work together. Your BAP has a form of users to submit data, ensure that the data is correctly stored in your data. The third one is user testing. Have someone use your project and give feedback. For example, ask a friend to test to do list app and tell you if anything feels confusing or if anything doesn't work. Tools for testing. For automated testing tools, you can use Lenium for WebAps and Pi test for Picon projects to automate repetitive example, if you're building a recipe app, here's how testing might look like. The first one being unit testing, ensure your search function works for different queries like pasta or chicken. Integration testing, you can test that the users can enter ingredients and recipes and are displayed out correctly. Third one being user testing, have someone search for a recipe and see if the app feels easy to use. In this lesson, we have covered how to apply the skills that you have learned to start building your project, use Git and collaboration tools like Github to track changes and work with others. Test your project to ensure that it works correctly and delivers a great user experience. Next step is to start coding and testing. Remember to take one step at a time and enjoy the process of bringing your ideas to life. You have got this, and I will see you in the next lesson. 43. Presentation and Feedback: Congratulations. You have reached the final stage of your Capstone project, the presentation and the feedback. In this lesson, we will learn how to effectively present your project and gather valuable feedback from your peers and your instructors. We will cover two important topics over here, presenting your project to your peers or instructor and also peer review and feed. The end of this lesson, you will have the tools to showcase your hard work with confidence and learn how to use feedback to improve yourself. Let us start with presenting your project to your peers and instructors. Why presentation Nas. Presenting your project is your chance to share your work with others. Explain the problem that your project solves, show off your skills and creativity, receive constructive feedbacks to make your project even more better. Remember, a good presentation isn't just about the finished product. It's about telling the story of your journey, how to structure your presentation. Follow a very simple structure for your presentation. The first one me introduction. Start by introducing yourself and your project. For example, I am Alex and I have built a recipe recommendation app that helps users find meals based on the ingredients that they have next, the problem. Explain the problem that you're trying to solve, for example. Many people struggle to decide what to cook, especially when they have only few ingredients at home. My app solves this by suggesting recipes using the available ingredients. Next, your solution. Describe your project and how it solves the problem. For example, users enter their ingredients into the app and it displays matching recipes. It also saves favorite recipes for future uses. Next, the demo. Show your project in action. Walk through key features step by step. For example, demonstrate entering ingredients, viewing recipes and saving the favorite. Next, challenges, briefly discuss any changes that you have faced and also how you overcome them. For example, I had difficulty implementing the recipe matching algorithm. But after researching string similarity technique, I was able to make it next conclusion. Wrap up by summarizing your project and thanking your audience. In summary, for example, in summary, this act makes meal planning easier for busy people. Thank you for listening, and I would love to hear your feedback. Here are some effective tips for your presentation. First one, bet and fun size. Avoid technical jargons. Speak in very simple terms. Next, use visuals. If possible, use slides or diagrams to explain the cosptthirty practice. Rehearse your presentation to be confident and organize. Lastly, engage with your audience. Ask questions like, have you ever struggled with the problem your project solved? For example, Imagine that you are presenting a todo list app to your classmate. Instead of diving straight into the demo, start with a relatable question. How many of you have forgotten an important task because you didn't write it out? This grabs attention and sets the stage for your app as a solution. Next, we move on to peer reviews and feedback. Why is feedback important? Feedback helps you identify areas for improvement, learn how others view your project, and also gain a new perspective to your ideas. Even the best project can benefit from constructive criticism. How to ask for feedback. Firstly, be open minded. Remember, feedback isn't personal. It's about improving your work. Next, ask specific questions. Like instead of asking, what do you think? Ask targeted questions like, is this app easy to use? Do you think that the recipe suggestions are helpful? What features could I add to make it much better? Also, listen carefully. Pay attention to both positive and negative feedbacks. Take notes during the feedback session. So giving feedback to others is important. While reviewing your POS project, be kind. Start with something positive. Be specific. Point out exactly what you like and what needs to be improved. 30, be helpful. Suggest actionable improvement. For example, you can say something positive like this. I love how simple and user friendly your app is. The recipe suggestion loads quickly, and the interface is very constructive part of the Calv, one improvement could be adding a filter for vegetarian recipe. That way, user with specific diet can find yields more easily. Next, how to use this feedback. Firstly, review the feedback. Go through the feedback that you received and prioritize the most valuable suggestion. Next, make adjustments. Implement changes based on your feedback. For example, if users find a feature confusing, improve the design or add instruction. Thirdly, test again. After making the changes, test your project to ensure everything works as expected. Activity that you can do over here is pair up with your classmate or a friend, present your project, and ask for feedback. Then review their project and provide constructive feedback. Thirdly, discuss what you both have learned from the experience. So in conclusion, so in conclusion, presenting your project and receiving feedbacks are the key steps in Cape Stone process. Today, we have learned how to structure and deliver an engaging presentation, how to ask for and give constructive feedback. I would like to point out over here that your project isn't a demonstration of your skills. It's a learning experience. Use feedback to refine your work and take it to the next level. Congratulations. You have made to the end of this course. I hope this journey has given you strong foundation in computer science and the confidence to keep exploring. Remember, learning tech is a marathon, not a sprint. Keep practicing, stay curious and never stop building. Your next step is to apply what you have learned, whether it's working on projects, contributing to open source, or preparing for your first tech job. I'm excited to see where this knowledge takes you. Keep pushing forward and welcome to the world of tech, and I will see you in the next learning adventure.