Data Science Course For Beginners 2026

1. Introduction To Data Science Course: Welcome everyone to the first class of complete introduction to Python for data science. For the data science, we are going to need the Python programming language because Python is powerful open source programming language for the data science. So for most of the libraries for the data science, that are all present in the Python language. So that's why we are going to need Python for the data science. So after that, we are going to discuss about what are all the concepts in data science. The first one is the Num Pi. So Num Pi is powerful open source for scientific computing for the Python language. So that's scientific computing like mathematical equesence and mathematical formulas. For that, you are to need the numPis and also if you want to create multidimensional array, you to need the NumPi. So that's the use of Num Pi. And then the third one will be the Pandas. So Pandas is also a powerful open source Python library, which can be used for data manplaan and analysis. For the datasets like CSV file, Excel file, we are going to need the data analysis. For the data analysis, we're going to need Pandas. So Pandas is present in the open source library of the Python. And then we are going to discuss about MAD plot lip in Python. So MD plotlip is also one of the open source library in Python, which can be used for data visualization. So the data visualization like bar chart, Pichart and graph scatter plot, lots of plots are available in MAD plotlip for the data visualization. And then CBN in Python, CBRN is also one of the data visualization library present in the Python. So there are a lot of plots available in CBN compared to MAT platip. So the plots scatter plots, heat maps, violin blots, bar plots, pipe plots. So there are a lot of plots in Sabar. So C ban is very, very useful for data visualization. After that, we are going to discuss about the advantages of data science. And the first one will be that enhanced decision making. So data science helps in data analysis and data visualization. From that, we can decide our output. So that's the use of decision making with the help of data science. And the second one is the improved efficiencies. We can improve the other techniques using that data science. We can also improve the accuracy, precisions of the data science. And the third one will be the competitive advantage. So there are a lot of advantages when you learn data science. And the fourth one will be the innovation and disruption. So data science can be useful for new innovations and new trends in the upcoming future world. So that's it. We discussed about the importance of Python in data science. For data science, lot of libraries are present in the Python programming language. So that's why we are going to need Python programming language for data science. So in the upcoming classes, we are going to discuss about Num Pi, Pandas, MD plotlip. So let's see in the next class. 2. Pandas Class 1 : Import Dataset: Welcome everyone to the first class. In today's class, we are going to discuss about how to import the dataset for the Pandas, and also we are going to discuss about how to install the pandas package and how to import the pandas package. For the data analysis like CSV Excel five, we are going to need the help of Pandas. Also, Pandas is the library present in the Python programming language. So first, we are going to discuss about how to import the dataset for the pandas. In the Google Colab, you can see the upload option, you have to select that option. And then how to select your CSV file. So CSV file full form is nothing but comma separated values. So you have to import the CSV file, and then how to select and open the file. So in that, you can see the columns like duras and date pulse, max, pulse, calories. So this CSV file will be useful for our pandas. So we discussed about how to import the dataset for the pandas. And then we are going to discuss about how to install the pandas package. For that, to type PIP install pandas. So before that, you have to add the symbol and then you have to run the code. So you can see my Pandas is already installed in the Python 3.10. So that's the step for installing the pandas. And then we are going to discuss about how to import the pandas package. You to type import pandas as PD. So PD is nothing but sort form of pandas. So you have to run, and then you can see so you have to run the code. So you can see your pandas package is imported successfully. So we are going to discuss about how to read our CSV file using the pandas package. For that, you have to create a variable data equal to PD dot. PD is nothing but pandas. Read, you have to type read hyphen CSV off. Inside the quotation, you have to type the CSV file name. And then format data dot CSV. You have to run the code, so you can see your Pandas data set is imported. So that's it. In today's class, we are discussed about how to install the pandas package, and also we are discussed about how to import the dataset for the pandas. 3. Pandas Class 2 : Info Function: Welcome everyone to the third class. In today's class, we are going to discuss about information function in Pandas. So the concept of information is nothing, but it prints the information of our CSV file. So what are all the data types? What are all the columns available in the CSV file? So how to type print, so how to type data dot info off. So you have to run the code. You can see the information of our CSV file. So there are five columns, duration, date, pulse and max pulse, 32 entries 0-31. So our class will be data frame. And then you can see the data types of the columns, integer, object, float, and also the null values, and then the memory usage. So you can see the To bit. So that's the use of information in Pandas. 4. Pandas Class 3 : Head & Tail: Welcome everyone to the second class of pandas. In today's class, we are going to discuss about head and tail function in pandas. So first, we are going to discuss about head functs and in pandas. So the concept of head is nothing, but we are going to print top ten values or top five values using the head function. So we are going to discuss an example, so we have to print data, dt, and then we have to type head function. So we have to type head off. So in default, the output will so top five values of the CSV file. So you can see in the output, top five values are printed in the output zero to four. So 00 is the first index. So default head value, print, top five values. So if you want to print 20 or ten inside the head, you have to type the value. So inside the head, you got to type the value 20 or ten or five. So then you have to run the code. So you can see zero to 19 values are printed in the output. So these values are present in the CSV file. So that's the use of head function. It prints top values. And then we are going to discuss about tail function in Pandas. So tail function is opposite to head function. Head function prints top values, tail function prints bottom values. So we are going to see an example how to print data dot. So you how to print, data dot, tail off. So in default, bottom five values will be printed in the output. So you have to run the code. So you can see bottom five values are printed in the output, 31227. So there are tat one values present in our CSV file. So if you want to print 20, how to type 20, and then you have to run the code. So you can see bottom 220 values printed in the output, 312 toll. So that's the use of head and tail function. Head function prints top five values are top ten values, Tai function prints bottom five values are ten values. 5. Pandas Class 4 : Drop Duplicates Function: Come, everyone. In today's class, we are going to discuss about how to remove the duplicate row from our CSV file. So in our CSV file, the last two rows are duplicated. For that, we have to remove the duplicates from our CSV file. So you have to type DF. So that's the data variable, DF dot, drop, drop under scool duplicates. So that's the fonts and to remove the duplicates. And then we are going to print our CSV file. So in the output, you can see the two last rows duplicated are removed. Only one row is present, other duplicate value row is deleted. Two rows are not deleted, only one duplicated row is deleted. So that's the use of drop duplicates. So if you want to remove duplicates from the column, you have to type the code for that. For that, we are going to remove the duplicate values from the pulse column. So again, you have to type df dot, drop, underscore duplicates of inside that, you have to type the funct and subset, subset equal to, and then you have to type the column name. Column name can be pulse from the pulse, all the duplicate values are removed. So the duplicate values like 103, 108 will be removed. So that's the use of drop duplicates in the subset function. So you can see only less amount of rows are present because all the duplicate value rows are deleted. So in the other columns, the duplicate value will be present. But in the column of pulse, no duplicate value will present. 6. Pandas Class 5 : Dropna Function: Come, everyone. In today's class, we are going to discuss about dropna function in Pandas. So dropna function is very, very useful for. If you want to delete all the null values present in your dataset, we are going to need the help of drop nu function. So in our CSV file, there are a lot of null values are present in our columns. So the null values in the row 20, you can see the null value, and also you can see the null value in the row 24. So there are a lot of null values present in our CSV file. We are going to remove the null values from our CSV file. Before using the drop nu, you have to remember that by using the drop nu function, whole row will be deleted. So you have to remember that drop nu deletes whole row of the null value. So we are going to see an example how to delete the null values. So you have to type data equal t. So that's the variable which holds the CSV file, and then you have to type data dot dropna, that's the function. So drop null values. So after that, we are going to print our CSV file, so that's the data. So if you run the code, so you can see all the null value rows are deleted. So you can see there are no null values present in our CSV file. So that's the use of dropna function. If you use dropna function, it completely deletes the null value row. So in our CSV file, there are 33 rows are present. So you can see 33 rows are present in our CSV file, original CSV file. So after the usage of dropna function, only 30 rows are present in our output because the null value rows are deleted from our CSV file. So in the next class, we are going to see about how to fill the null values with random values or own values. 7. Pandas Class 6 : Fillna Function: Hello, everyone. In today's class, we are going to discuss about fillna function in Pandas. So fillna function is opposite to dropna function, so dropna completely deletes the null value rows. But in the fillna function, we are going to fill our own values or random values in the null value rows. For that, only we are going to need the help of fillna function. So we are going to see an example. So you have to type data equal, data dot, fillna. So you have to type the function fillna. So inside the bracade have to type your own value, which you replace the null value. So thousand will replace all the null values in your CSV file. So after that, we are going to print our CSV file. So in the output, you can see in the null value place, thousand will be replaced. So you can see, all the null values are replaced by 1,000. So if the data type is integer, thousand will be present. If the data type is float, zero will be added. 8. Pandas Class 7 : Replace Function: However, in today's class, we are going to discuss about how to replace the values in the Pandas data set. For that, we are going to see an example how to replace the value with another value. First, we are going to print our CSV file. So in that, you can see all the values of the CSV file. We are going to change the value 60 in the duration column. With another value. For that, you have to type Dt, that's the data, and then you have to type the column name duration, dot replace function, and then you have to type the values to be replaced. For replacing, we are going to use the dictionary. Key value pair, 60 will be replaced by 100. You can type your own values. Come in place equal to true. So in place equal to true is nothing, but we are going to so we are going to change our value in the dataset. Say out to run. And then we are going to print the value of DT So so so you have to remember that for the integer in the dictionary, you don't have to type the quotation. So you have to remove the quotation because in dictionary, integer does not have quotation. So after that, you have to run the code. So in the output, you can see all the 60 values in the duration column are changed to 100. So you can see all the 60s are changed to 100. So that's the use of replace function in Pandas. If we want to replace any value with another value, we have to use the replace function. 9. Numpy Class 1 : Presentation: Welcome everyone to the first class of complete introduction to Numbi. So NumPi is powerful open source library, which is present in the Python programming language. So Numpi can be useful for scientific computing. So the scientific computing like finding the values of the mathematical operations, mathematical for las, and also we are going to need NumPi for creating multidimensional array and matrices. For that, the numPi is very, very, very useful. But also, we are going to discuss about the concept, why the numPi is faster than list. In list, the values are stored in discontinuous places. But in the case of Num Pi, the values are stored in continuous places. So that's the most important difference between Num Pi and list. For the discontinuous place, the indexing concept is very, very, very slow. For that, only the list is slower than NumPi. And also the numPi can be used for the low level languages like C and Foton. So we can use NumPi for more memory efficient than Python list. So why the numPi is faster than list is nothing but NumPi stores the values in continuous place. But in list, the values are stored in discontinuous place. For example, if you want to search any value in the list, it takes more amount of time. But in the case of NumPi it takes only less amount of time because of continuous place. So there are many types of array in Nampi we are going to discuss one by one in the coming class. So the first one will be the zero dimensional array and then one dimensional array, two dimensional array, three dimensional array, and then multidimensional array. So there are many data types in Numpi so we can use integer data type, float data type, Boolean data type, complex data type, object data type. So there are three advantages of Numpi high performance, memory, efficiency, vectorized operons. So that's if we completed the introduction to NumPi In the upcoming class, we are going to discuss about how to create a NumPi and how to create a functions in NumPi 10. Numpy Class 2 : Import Package: Welcome, everyone, to the second class of NumPi. In today's class, we are going to discuss about how to install Numpi in Python, and also we are going to discuss about how to import the Numpi package. For that, first, we are going to discuss about how to install NumPi for that, you have to type the command. You have to PIP install Num Pi. So that's the syntax for installing Num Pi. In Google Collar, you have to type symbol before PIP. After that, only your output will execute. So in other IDs, you don't have to type symbol for installing the packages. In Google Colab, you have to type symbol for installing the package. NumPi is already installed in my computer. So you can see, Python 3.10 already installed the NumPi package. So after that, we are going to discuss about how to import the NumPi package in Google collab. For that, you have to type the code, Import, and then you have to type the package NumPi as NNP. We are going to call NumPi as NP. You have to run the code for importing the package of NumPi. So in the upcoming class, we are going to discuss about how to create NumPi array. 11. Numpy Class 3 : Multi Dimensional Array: Welcome, everyone. In today's class, we are going to discuss about the types of NumPi array. For the first, we are going to create zero dimensional array, and then we are going to create one dimensional array, multi dimensional array. First, we are going to create zero dimensional array, to type N equal t, Np NP is nothing but NumPi then to type array of. Inside that, you can type your own value. I'm going to type one. If you run this code, after that, you have to print the value of N. And then you have to run the code. You can see one. So this is the syntax for creating zero dimensional array. So after that, we are going to discuss about how to create one dimensional array. For that, we are going to type N equality NB dot array of. Inside that you have to type the square bracket, and then you have to type the value one command, two. So that's the syntax for creating one dimensional array. For zero dimensional array, you don't have to type square bracket. For one dimensional array, you have to type square bracket. So one square bracket equal to one dimensional. To square bracket, it will be considered as two dimensional. The square bracket will be considered as three dimensional. For the simple way of understanding num Pi dimenson you have to remember the square brackets. One square bracket will be considered as one dimensional array, one dimensional num Pi array. So after that, we are going to discuss about how to create two dimensional array. For that, you have to create an variable N equal to NB dt. So NB is nothing but NumPi dot array of. Inside that auto type two square bracket. After that, you have to time the value one comma two. Come on, and then you have to type the second values to comma five. So that's the syntax for creating two dimensional array because there are two square bracket. For the simple way to understand, you have to remember the square brackets. So after that, we are going to print the value of N. So you can see the output one comma two and two comma five. So after that, we are going to discuss about how to create three dimensional array. For that, we are going to create a value N equal t NP dart, array of. So inside that, you got to type three square bracket. So three square bracket is nothing but three dimensional. And then you have to type the values. So you can type your own values, three comma four. So that's the syntax for creating three dimensional array, three square bracket four, three dimensional. After that, we are going to print the value of N, so you can see one comma two and three comma four. So that's it. In today's class, we are discussed about how to create three dimensional, two dimensional, one dimensional, zero dimensional NumPi array. 12. Numpy Class 4 : Ndim Function: Welcome everyone. In today's class, we are going to discuss about dim function in Nampi. So if you don't know how to find the dimensons of the array, we are going to need the dM function for finding the diamond sun of array. For that, we are going to see an example. We are going to copy and paste the three dimensional array from the previous class. So you to copy, and then you have to paste. We are going to find the dimenson of this array for that you have to print inside that to type dot dN dt N d so if you try to run the code, it will generate the diamondsons of the array. So the diamonds on will be three. So in the previous class, I told you that three square bracket comes, it will be considered as three dimensional. If you don't know how to find the diamond sun, you have to use dim function. 13. Numpy Class 5 : Ndmin Function: Welcome everyone. In today's class, we are going to discuss about N minimum function. So minimum function is very, very useful for. If you want to create multidimensional array, you have to use minimum function. For that, we are going to see an example. We are going to create ten dimensional array. So for that, you have to type A equaltiveNp dot array of first, we are going to create one dimensional array. So you have to type one square bracket. Inside that, you have to type the value one command, two, and then we are going to convert that one dimensional array into ten dimensional array. So you have to type N minimum equal to ten. One dimensional will be converted to ten dimensional array. So after that, you have to print the value of A, and then we are going to print the dimensional of the A. At N minimum. So in the output, you can see ten square brackets are created. First, one dimens null is converted to ten dimens null. For one dimensionll one square bracket, for ten dimens null, ten square bracket. So you have to count the number of square bracket, so it will be considered as ten square brackets. And also, you can see the diamonds null. So diamond cell is changed from one dimens null to ten dimens null. So that's the use of N minimum. 14. Numpy Class 6 : Nditer Function: Welcome, everyone. In today's class, we are going to discuss about how to iterate the elements one by one, using the NDT function. For that, we are going to see an example how to iterate the elements one by one. So you have to create a NumPi array N equal to NP that array of so we are going to create an array, one comma, four, comma, six, comma three, comma six, come three, come eight. You can type your own values. After that, we are going to iterate the elements. For Irata elements, we are going to need the R loop for I N, and then you have to type Np dot. You have to call the function N dicta. Of A. So that's the array, and then we are going to print the value of I. So by doing this, we can call the elements one by one. So output will one, four, six, three, six, three, eight, two. So The function is wrong. You have to type N d. You have to remove one T from the function. So you have to remove one T from the function, and then you have to run the code. So you can see the values of the Numbi RA itrated one by one. Also, you can use the ND or for finding the E numbers and odd numbers. Okay. For that, we are going to see an example. You have to use the I condition if I divided by two equal to equal to zero. So the value of I divided by two, the remainder gives zero, it will be considered as E one number. So what are all the values divided by two, which gives the remainder zero, that will be considered as even numbers. So if you want to find the R numbers, you ought to change the naught equal to zero. So in the output, you can see one, three, and three. So four, six, six, eight, two, these are all the even numbers. That's it. In today's class, we discussed about how to iterate the elements one by one using the Nd function. 15. Numpy Class 7 : Search Sort Functions: Welcome everyone. In today's class, we are going to discuss about what are all the functions available in NumPiF the first, we are going to discuss about copy function in umpi. Copy function is nothing, but we are going to create an array from the original array. For that, we are going to create an array, X equal te Np dot, array of. We are going to create single dimensional array one comma pi. And then we are going to create a new array with a copy of X. For that, you have to type the variable name dot copy of so the variable will be X. And then we are going to print the value of X and T, you have to run the code. So in the output, you can see the value will be copied from the original array. IT copied from the X array. Same values one comma pi, one comma pi. So that's the use of copy function. So after that, we are going to discuss about how to change the values in the numpi. For that first, we are going to create an NumPi you have to type A equality Np dot array of. We are going to create one dimensional array, one comma four. And then we are going to print the value of A. So if you run the code, you can see the values one comma four. After that, we are going to change the value. You have to remember the index concept. One will be the index of zero. So you have to type a square bracket of inside that To type the index position one equal to zero. So one will be the index position of four value. We are going to change the value of four. And then we are going to print the value of a RA. So we can also change the index positions. Whatever values if you want to change, you can change the values. So in the index position of one, four change to zero. Also, I'm going to change the value of zero index. So the zero the index is nothing but one, so it will be changed to zero. So it will be changed to zero. Zero comma seven. So if you want to change the values of the umpi array, you ought to use the change function, and also you have to remember the index concept. So this is the example for one dimensional. After that we are going to discuss about how to change the values in the two dimensional array. For that, you have to create two dimensional array N equality NP tat array of two square bracket. Inside that you have to type the values, one comma five, and then you have to type the second array value, three comma six. So we are going to print the value of NF one comma five is the zero array. Three comma six is the first array in the two dimensional. Also in the zeroth array, one will be considered as zeroth index. Five will be considered as first index. So in the first array of the two dimensional, three will be considered as zeroth index. Six will be considered as first index. So you have to remember the concept in the two dimensional array, we are going to change the value zero. That's the array. Zeroth array, that is one comma f. And then we are going to change the value of the index. So in the zeroth array, the zeroth index will be one. One will be changed to Nian so zero is the array. And then comma zero is the index position. So if you run the code, if you run the code, so you can see the values are changed. So you can see NO is changed to NN. So for the convenience, I'm going to remove all the previous code and run the code. So you can see values of the first array changed to values of the second array, zeroth index that is zero array value changed. After that, we are going to discuss about sorting function in Numpi. So sorting can be ascending order and descending arder. So we are going to create a NumPi Aequaltnbt array. We are going to create one dimensional array to comma one. And then we are going to print the value of A. So after that, I'm going to sort the values for that, you have to print NP dt short. So that's the function. Inside that you to type the array name, and then x is equal to zero. For Ax is equal to zero, it will be sorting in the column Vise. For x is equal to one, it will be sorting in the row Vs. For one dimensional, column sort will be possible. For two dimensional only row SAT will be possible. So you have to print the value. So in the output, you can see two comma one is changed to one comma two. For the one dimensional. After that, we are going to create sorting for two dimensional. For that, you have to type double square bracket, and then values, three comma one and five comma seven are your own values, six comma two. After that, we are going to sort the values, and then we are going to print the value of NB do sort of, you have to type the value of the array, X is equal to one. X is equal to one is nothing but row is sorting. In two dimensional. So three comma one will be changed to one comma three. Six comma two will be changed to two comma six. So that's the row y sorting, one comma three, and then two comma six. So that's it. In today's class, we are discussed about how to sort in the row s and the column is. So after that, we are going to discuss about loss function in NumPi that will be searching the elements in the numPi. We are going to discuss an example. We have to create a NumPi array. We are going to create one dimensional array, one comma, two. We are going to find the value of two. So value of two will be generated in the output in the form of index position. For that, we are going to create an function X equal Np dot, you have to create a function A equal, equal to two. So in the output, it generates the index position of the value two. You have to print the value of X. To remember in the search output value will be generated as index position of the value. So in the output, you can see one because the index position of two in the NumPi RA is one. So that's why output source one. So if you want to find the index value of the one value, you have to change the value and then run the code. So in the output, you can see the index position zero. So the value of A is present in the index position zero. 16. Numpy Class 8 : dtype Function: Everyone, in today's class, we are going to discuss about D type function in NumPi. D type is very, very useful for if you want to find the data type of the NumPi array, you ought to use that D type. For that, we are going to see an example. So you have to type N equality Np dot array of, we are going to create one dimensional array, and then we are going to find the data type of the numPi. For that only we are going to need the D type function. You have to type N dot D type. That is nothing but data type of the Numpi. So it will be generated as integer 64. So one comma two is nothing but integer data type. Also we are going to discuss about typecasting in Numpi. For that, we are going to convert the integer data type to float data type for that oto type D type equal to float. We are going to convert the integer Numpi array to float. So in the output, we can see point value is added for converting the integer type to float. Also, you can tie other functions of the float So you can see 16 to 56 Taty 264, 80. So there are many types of float. So you can also use Own float for your project. So the D type can be used for both finding the data type of the Numbi array and also typecasting for the Numbi. Also you can change the integer data type to string data type using the D type function. So you have to type string underscore for converting the integer data type to string data type. That's it. In today's class we discussed about D type in NumPi. D type is useful for finding the data type and also typecasting. 17. Numpy Class 9 : Concatenate Function: However, in today's class, we are going to discuss about how to combine two or more array in umpi. For that, we are going to create function concatenate. So concatenate is useful for combining two or more array. First, we are going to create an array N equality Np dot array of. We are going to create one dimensional array, one comma four. And then we are going to create second array Np dot array of two comma five. We are going to combine these two array using the concatenate function. For that, we are going to create a new array B equal to NP dt, concatenate of so inside the bracket, you have to type the values of the array. So the array variable will be A and N. So you have to type double bracket, AN. And then we are going to print the value of B. So in the output, you can see the array two comma pi, and then N array one comma four are combined in the array of B. So that's the use of concatenate function. 18. Numpy Class 10 : Arrange Function: Welcome everyone. In today's class, we are going to discuss about another function in NumPi that is A range function in numPi. So if you want to create random range of NumPi array, you have to use the Arange function. So we are going to create an array in the range N equal t NB dart, and then you have to type the function A range. A range will be the function for creating the range of array for that, you have to type the value ten. We are going to create ten value NumPi RA. So if you try to run the code, you can see zero, one, two, three, four, five, six, seven, eight, nine, that's the range of ten. So you have to remember the concept of index. So the index starts from zero. So zero to nine, it will be considered as ten values. We are going to see another example, we are going to create the range of 20, so you have to type 20 code, so we can see zero to 19 values are present. So 19 values are present because zero starts. So that's the first index. In today's class, we discussed about A range punks and in Python. So A range is nothing, but we are going to create the range of RA. 19. Matplotlib Class 1 : Import Package: Welcome, everyone to the first class of Matt Plat Lip. In today's class we are going to discuss in today's class, we are going to discuss about how to install and import the package of Matt Platlip. So matplot lip is very, very useful for data visualization. So the data visualization like creating graph, creating a bar chart, creating a pipe chart or creating a scatter plot, we are going to need the help of Mat plotlip. So first, we are going to discuss about how to install the package for that you have to type pip install, and then you have to type the package name MD plotlip. So you have to run the code. So you can see, MD plotlip is already installed in my Python three pine ten. After that, we are going to discuss about how to import the package for MT plot you have to type Import. So that's the keyword input, and then you have to type the package name matplotlib dot, we are going to use the sub package Pi plot for creating the bar chart Pi chart. As PLT, we are going to call matplot lip as PLT. Pi plot is one of the sub package in matplot lip, you have to run the code, so you can see package is imported successfully. So after that, we are going to impo the dataset for the mad plot lip. For the data set, we are going to need the help of pandas. So you have to type Pandas import pandas as pd. And then we are going to read our CSV file, which have been discussed in the previous Pandas course. You have to type the value data equal to. So you have to type the variable data equal to pd dot, read, underscore CSV. So that's the syntax for reading the CSV file. You have to type the CSV file name. So you have to run the code. So that's it. So after that, we are going to print our CSV file. So in the output, you can see the CSV file columns and values. In today's class, we are discussed about how to install the MD plotlip and how to import the MD plotlip package and also Panda's package. 20. Matplotlib Class 2 : Title Function: However, in today's class, we are going to discuss about how to create title for our graph. So in the previous class, we discussed about how to create title for As and Vyxs. In today's class, we are going to create title for our graph. So we are going to use the same graph created in the previous class, and then you have to type the function title. For that, you have to type PLT dot title of. So inside that, you have to type your own title so you can type colors and pulse graph. So after that, you have to run the code. So you can see in the output colors and pulse graph presented at the title of our graph. So this graph shows that it created for the color rays and the pulse from our CSV five. So that's the use of title function. If you want to create title, you have to use title. 21. Matplotlib Class 3 : xlabel ylabel: Welcome, everyone. In today's class, we are going to discuss about how to find the *** and Y axis in our graph. So in the previous class, we had discussed about how to create a simple graph. So in that graph, we cannot find the sis and Y axis properly. For that, we are going to need the help of X label and ylabel function. So X label is for the Acis and Y label is for the Y axis. So we are going to create the same graph we created in the previous class. So you have to copy and paste. And then we are going to type plt dot X label. For the naming of the X axis, you have to type the title of the Talis. So Caloris is the Acis. And then Y label is nothing but Yaxis and then you have to type the title of the axis. That is pulse. And then we are going to sow our graph using the so function. You to run the code. So in the output, you can see the title for the As and the Oaxis. So As is the calories, and the Yaxis is the pulse. 22. Matplotlib Class 4 : Linestyle & Linewidth: Welcome, everyone. In today's class, we are going to discuss about how to use line style and line width for our graph. So you have to copy and paste. And then in the graph, you have to type Camma, and then you have to type line style. So line style is very useful for the type of line. That is example like dotted line, strong line, dash line. So these are the types of line styles. First, we are going to see about dotted lines. So you have to run the code. So in the output, you can see our line style is changed to dotted line style. So in the previous output, it is single line. In today's output, it will be considered as dotted line. Also, you can create das line also. So in the output, you can see das dash das lines. And then we are going to see the last solid. So solid is the default so in the previous class, we have created the solid line style. So after that, we are going to discuss about line width. Line width is useful for increase the width of the line. So I have to type line width equal 20 line width equal to 20. So our line width will be increased by 20. And then you have to run the code. In the output, you can see line width or increase. So if you want to decrease, you can type decrease value. Ten, we are going to find the ten. So ten also not suitable for our graph. So the correct value will be three So three also suitable for our graph. So that's the use of line width and line style. 23. Matplotlib Class 5 : Marker Function: Welcome everyone. In today's class, we are going to discuss about how to create a marker in our graph. For that, we are going to discuss an example. In the previous class, we have created the simple graph. Inside that you cannot see a marking point of the XSS and Ss. For that, we are going to need the help of marker function in math have to type PLT dt. So PLT is the matplot lip plot off, you have to type the columns data inside the square bracket, you are going to create colors as XAs and data of and then you have to type the second axis Y axis value, pulse, and then you have to type the marker function. Marker equal t, you can type your own symbol. So first, we are going to create a marker of star. And then you have to create a so function to display the graph. So you have to run the code. So you can see the marker is placed between both Xs and Ys intersection point. So you can see 201110. So marker is placed, and then you can see the values placed between Xs and Yaxs intercept. So can also create your own symbol of marker. Whatever symbol you type inside the marker, it will generate in the output. So most of them uses point for the marker. So we are going to use the point. So in the output, we can see the points are placed between the intercept. For the color is the As and the pulse is the OAs. So the color is started from 195 and ended with 480. So you can see the XA values started 200-400 or 550. And then pulse is the Oaxs started from 98 and ended with 120. So you can see the values started with 95 and ended with 130. So based on the values of the *** and ***, marker is placed between the intersect. 24. Matplotlib Class 6 : Show Function: Welcome everyone to the second class of Matt plot lip. In today's class, we are going to create a simple graph using the so function. So so function is very, very useful for displaying the graph. So you have to type PLT dot plot, and then you have to type the values of the CSV file. So you have to type the column name. So we are going to use the duras and column and then pulse column for creating our graph so you have to type inside the square bracket, you have to type the column name. So column will be duration. So you have to type, correct, and then and then theta equal, and then theta of pulse. And then we are going to use the so function PLT dot so off. So in the output, you can see the graph for the two columns, dursin and pulse. So in the X axis, it will be the dursin. The Y axis will be the pulse. So we have created the simple graph for our CSV file. So the points can be vary based on the values of the columns. 25. Matplotlib Class 7 : Barplot: Welcome, everyone. In today's class, we are going to discuss about bar plot in matplot lip. So bar plot is one of the type of plots in matplot lip. For that, we are going to see an example how to create bar plot in matplot lip. For that, you have to type PLT dt bar. So that's the syntax bar, o to type bar. Inside that you have to type the columns. So column will be calories. So you can type your own columns from the CSB five and then data, and then you have to type the second column pulse. After that, we are going to display our bar plate. You can see in the output, bar plot is created using the matplot lip. So if you want to increase the width of the line, you have to use width function in matplot lip. So you have to type with the equaltivet. So by doing that, the line width will be changed. So you can see line width is changed. So after that, we are going to copy all the values we are created in the previous class. So you have to copy and paste and then run the code. So you can say your title as oxis are created. So our bar plot is created using our previous class. So the syntax is nothing but dot bar. And then we are going to discuss about how to change the color of the bar plot. For that, you have to type the color equal to single hyphen. See how to type your wwn colors, whether it can be red, yellow, black or white, you can type your wwn color. After that, you have to print, you can see you changed the color of the bar plot. So you can also change the colors, whatever you want. So if you want to sew two or more colors in the bar plot, you have to create square bracket. Inside that, you have to type the colors. So you can see green and red sewn in the output. So after that, we are going to discuss about how to increase the width and height of our bar plot. For that, we are going to create a function that is figure size. So you have to type PLT dot, figure. And then bracket inside the bracket, you to type fix size equal open bracket. Inside that, you have to type the width under height. So width can be ten, height can be five. So after that, o to print, you can see your width and the height of the bar plot is increased. So figure size can be very, very useful for. If you want to increase the width and height of your bar plot or Pie chart or any other chart, you have to use the figure size. So in today's class, we discussed about how to create a bar plot in MT plotlip. So other chart can also be easily created in MT plot lip. Instead of typing the bar name, you have to type the Pie chart or scatter plot name. Then scatter plot Pie chart will be generated in your output. So matplot lip is very, very useful for data visualization. 26. Data Science Project: Class of Data Science project. For the Data Science project, we are going to create a new project called Google Search Analysis. So Analysis is one of the important topics in data science. For that only, we are going to create the project, Google Search analysis. So you have to open the folder with VSCode, or you can use any other IDE for your project. I'm going to use VSCode so after opening the VS code, you need to install the important extensions. So for running your project, you need to install this, so you have to select the extension. So in that first, you have to download the Jupiter extension. So you have to type Jupiter. So you have to wait further. So after that, you need to install this extension in your VS code. So you have to install this. So after installing the Jupiter extension, you need to install the Python. So for running our project, we need Python. So for that, you have to install the Python. So you have to install the extension of Python. So these two extension are very, very important for our project. So after that, you need to create a new file, and then you have to type the file extension IPYNB. So that's the extension for Jupiter. So you have to wait for the Jupiter extension to be loaded in your VS code. Okay, guys, we are going to create our Google Search analysis data science project. Before running our project, you need to install the important packages. So for that, you need to type, Pip install. You have to type, Pip install Pitrens. So Pitrens is very, very useful for finding the Google Trends data. So Google Trends contains all the details of the Google search. For that only, you need Pi trends for collecting the data from the Google Trends. So you need to install this package. Before that, you need to select your Python environment. That is Python interpreter. I'm going to select three Pi Nian. So after that only, you have to install the package. So after installing the package, you have to import the packages. So in the first class, we are going to discuss about how to import the packages. So first, we are going to import the package that is Pandas. So Panda is for data manipulation in data science. And then we are going to use the Pi trends for collecting the information from the Google Trends. We are going to request the data from Google Trends. For that only you have to type Pitrens dot request, Import trend request. We are going to request the data from the Google Trends. And then you have to import a matplot lip for data visualization, it is one of the important tool in data science. We are going to use the Pi plot for plotting our diagrams. And then we are going to use the variable for the trend Q so trend request request the data from the Google Trends. So Pandas for data manipulation, and then Pi trends for collecting the information from the Google Trends and then MT Plotlp for data visualization like creating the bar chart, Pi chart. And then we created the variable for trend request. You need to run the core. So you can see all the packages are successfully installed. So that's it. In today's class we are discussed about how to create a new file for our project, and also we discussed about how to install the Pitrens and also we discussed about how to import the packages. In the upcoming classes, we are going to analyze our Pitrens. So let's see you in the next class. Welcome, everyone. In today's class, we are going to analyze our Google search. So using the Pitrens so far that, we are going to see the example how to analyze it. So you have to type trans dt, build payload. You have to type, build payload of you have to type the keyword. That is keyword list. So if you want to find any important keywords from the Google search, you can use that. I'm going to search for the keyword data science. If you want to search for Missing learning, you can type Missing learning. If you want to search for any game, any movies, any musics, you can type those keywords. I'm going to search for data science. So you have to type data science. So trends dot inter interest by region. So interest by region is nothing but what are all the countries such as the word data sins frequently? So that's how we are to analyze. For that only we are using the interest by region. And then you have to create the variable data equal to data dot sort. We are going to sort the values in the descending order. So the first value will be the most searched word data sins by the country. So most words searched by the country. So the word is data science. So we are going to descending order the countries who are searching the word data since. So we have to type ascending equal to falls. So ascending equal to falls is nothing, but we are going to save the words by descending order. And then we are going to print the top ten values. After that, we are going to print the data. So that's it. So first, we are using the trends for finding the keyword data science. So we are going to search for the word data science in the Google search. And then by region, which country search the word data science. After that, we are sorting the countries by descending order. Most search value will be in the first and then we are going to print the data. So before running your project, you need to type Pi trends dot build. So trench dot bill will show error. For that, only we need to type PitrensPtrens dot bill load, and then interest by region. After that, you need to run the code. So you can see Zimbawave searches the word data signs most frequently. Then India, Ethiopia, Kenya, Singapore, China, and Nigeria. And also, you can change the keyword if I want to find the word signs, search by the most countries you need to change the code and run the code. Before that, you need to save the code. And then you can see, India searches the word signs most frequent. So that's how India in the first order and then Pili finds Ghana, Nepal. And then I'm going to change the word to artificial intelligence. That is AI. So we are going to find which country searches the word AI frequently. So you can see Vietnam searches the word AI frequently, and then China, Romania, Italy, Myanmar. So that's it. So that's how we can search our words using the patterns. Welcome, everyone. In today's class, we are going to discuss about how to provide visualization for our Google search analysis. So visualization is like bar chart, Pie chart. So for that, we are going to see the example for how to create those bar chart and Pie chart. So you need to type these codes. So you have to type data Equalter trends, request of Hutch heading. That's it. Heading h equal English. Us. Come on. T T is nothing but time zone. So time zone will be UPC. So that's it. And then we are going to and then we are going to use the payload, that is the keyword. So the keyword can be your own choice. So the keyword can be your own choice. I'm going to type data signs. So after that, we are going to create data equal to, data dot interest by interest over time. So we are going to use which year, which month the word is most frequently searched. For that only we are using the interest over time. And then figure comma xi is equal to Plot. That is Pi plot that subplots. We are going to create subplots in then size. And then you have to provide the size, figure size, figure size equal to. You need to type the width and height 15 Gamma toll. After that, you need to provide the column. So the column will be theta sines. Data of theta sine. So keyword data sines and then plot. We are going to create a plot. And then plot dot style. We are going to provide the style for our plot. So you need to type plot dot style dot of, and then you have to type the style. I'm going to type fi, I'm going to type the style 538. So this is one of the style present in the data visualization of Matt plot lip. And then I'm going to type the title for my data visualization. You have to type your own title, total searches. You can type your own title. And then we are going to provide the XAs title and then YSS title. For that, you have to type plot dot X label. X label is nothing but ASIS. And then you have to provide the title here. Xs will be here. And then YXis that is Y label of, and then YXis will be total count. And then we are going to sew our plot, so off. So that's it. We can use Mt plotlip for data visualization. First, we are created the language, that is English. And then we are created the keyword, that is data science. And then we are using the interest over time. So in which year the words are more frequent. And then we are going to create the subplots. You have to provide the width and height for the subplots. And then we are going to create the plot for our data science keyword. And then we are using the style that is 538, and then we are going to provide the title for Xaxis and then SS. At last we are just so our output. So you can see in the output in the year 2020 from 2024, the word data science is most frequent in the year 2022 to 2023. So between these two years, the word data science is most frequently used. So if I change the word to AI or missing learning, I'm going to type AI, and then I'm going to run the code. So you can see in the year 2023 to 2024, between these two ranges, the word AI is most frequently searched. So because most of the people are using AI nowadays. So that's why you can see the peak of the AI word. So you can type your own words, and then you can analyze those words using the Py trends. So that's it. Our project analysis is completed successfully. Using the Py trends, you can analyze the Google search. The first class we are discussed about how to import the packages. In the second class, we created the word AI and Mine learning are data signs used by the region that is the country which uses the word frequently. And then in the last class we discussed about how to create this visualization for the word made data science. So you can see the visualization over the years. And then you can see the count of the words. Hope you learn something from this course. If you learn something from this, please put positive review in the command section. So let's see on the next project.