Data Basics - Data Literacy for Beginners | Micah | Skillshare

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Data Basics - Data Literacy for Beginners

teacher avatar Micah, Helping you learn to utilize software

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Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Watch this class and thousands more

Get unlimited access to every class
Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Lessons in This Class

11 Lessons (29m)
    • 1. Data Basics Course Intro

    • 2. 01 - What is data?

    • 3. 02 - Is data related to computers? The origin of data

    • 4. 03 - What types of data exist?

    • 5. 04 - Applying data types, how do we use them?

    • 6. 05 - How can you best keep, organize, and structure data?

    • 7. 06 - How is data used?

    • 8. 07 - How is data used? pt. 2

    • 9. 08 - How do we capture and obtain new data?

    • 10. 09 - Ways data is used in businesses and organizations

    • 11. 10 - How to build your course project

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About This Class

When you finish this course you will gain a basic level of Data Literacy knowledge.

This course is perfect for those of you who want to understand how data works and is utilized on a very basic level before you begin training in data analytics, science, software development or any other IT field.

It's also a useful training course for students with a non-technical business education focus who want to understand how data can be employed in processes, automation and overall business development in the modern world.

You will understand the following concepts on a basic level:

  • What data analysis is
  • What data visualization is
  • Where data can be obtained
  • How data can be captured
  • Common use cases for data

Software requirements (all free tools)

Google Slides

I am always here to help! Please submit comments and ask me questions here on SkillShare.

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1. Data Basics Course Intro: Hey, my name's Micah and I'm going to be your instructor for the data basics course. This course that I created is going to be a simple walk through, is going to take you from 0 to an intermediate level of data understanding so that you can pursue higher forms of data education like data analytics, data visualization, and data science as a whole. So what we're going to be learning in this course is going to be what data is, what different types of data exist, how data is used and utilized throughout multiple different organizations, and where you can find it. And then at the end we're going to do a project where we're going to identify a use case for data and that can be within your organization or your company. I'm going to help you think through how to use an identified data in the world in a way that can be useful for you. So look forward to having you in the class, and I'll see you in the next video. 2. 01 - What is data?: So let's talk about what data actually is. Any type of information can be considered data. Some examples of this that you'll find in everyday life are the number of steps that you take in a day. That can be a data point that can help you with your health or your fitness. The amounts behind transactions in your bank account that can be a datapoint to for your budgeting, accounting, your spendings. That way you have money for the things that you need. So that's the data, that information, those raw numbers in that table in your bank account and then things that I'm talking about or how that data's actually employed. So if you think about information that's being employed in a way, you can usually highlight where the data is. Something like a form for an entry that you would fill out for a raffle to win a free car. That's a piece of data which later gets consolidated into a database where all of the people who entered that raffle will all be put in a table and then they'll pick the winner. Addresses of business locations in your local area. The catalog of data right there can help you figure out what business to go spend your money. Yeah. And if that data wasn't structured and identified and curated and provided to you, you wouldn't be able to find where those businesses were. Another example would be the strength and area reach of a radio station signal. So you can actually measure with a strong degree of accuracy how the, how far the reach is for radio station. So if you've ever listened to the radio and heard them say something about we reach these areas. That's because someone has actually measured the data coming off of a sensor that indicates how strong the radio tower is and when that transmission just isn't quite strong enough to get through to those cars that are outside of those radios outside of that area. And then lastly, just an interesting example, agricultural usage. So the diameter of pumpkins growing in Alaska, you can measure that every day. Archive that data so you can know how big pumpkins good or how fast they get as big as they do in a place like Alaska where they can grow to be really, really large. So all of these are examples of data, and these are just some simple ones that you can find every day, every organization and every business has some type of data that they are utilizing. And I encourage you just think about all the information you use on a daily basis and you'll find pieces of data kinda everywhere. 3. 02 - Is data related to computers? The origin of data: So a question that I get asked a lot is data directly related to computers? And the answer is absolutely not. Data existed way before computers. Data is just information and information. It's been there before computers. Computers are just tools that allow us to work with data more effectively and make it more useful. Some really cool examples of data before computers include There's one really interesting ones. This one is ancient, actually. There's cuneiform tablets, which are mud and straw tablets that are smushed together. And then people will flatten out the surface and they would write on them with sticks. And they would draw characters in humans called cuneiform. Back in ancient Assyria, merchants would track their counting data on these mud tablets. So each of these tablets represented a table that held information regarding their golden silver transactions. So this is an example of structured data put in use by a business as far back as I can't even remember how far back it is, but ancient, ancient human civilization data has been around and computers only been around in the past a 100 years. Another interesting example is US immigration data in the 160s was kept in a paper report that was consolidated from multiple different entries. Whenever an immigrant would try to come into the country, the officer, whoever was helping them through customs and immigration would take down, hey, what's your profession and how old are you and what's your home country? And then all of that data is later log into a massive report for the US government As kept all on paper. But when you see the example of this, we'll see a very structured, it looks like an Excel sheet, but this was way before computers. So you don't have to be good at computers or technology or know how to code to work with data. Really, you just have to understand how information is organized and how it's used and what to use it for. But I hope these examples help to translate and answer that question for you. Data is not directly related to computers. However, it is a core computed a core component of how computers work. Function and we can use computers to make data better. 4. 03 - What types of data exist?: So what types of data exist? This is a great question. So one that you'll see commonly is numbers. So both whole numbers and decimal numbers are very common datatype. And you can use these to describe a number of different things. You could describe how old you are, perfect number, how tall you are also a number, but you can describe an inches as a number. The number associated with the inches of rainfall that an area that area got something even like a zip code would be an example of number data in action. Text data, which is also referred to as strings. And many programming and data use instances texts and string are kind of interchangeable terms. Texts is a very common datatypes. So your name is a text datatype, your first and last name, if they're separated, those would be different uses for text datatype. You could also have a status like paid or unpaid on an invoice. And that would be a use case for text datatype that would help you differentiate between the paid invoices and the unpaid invoices you have sitting in your accounting or accounts receivable, department. Dates and times are other interesting data type that it have their own little their own little usage is very important one, this is the temporal area of data. So what time and date, when, when the when component, when did this happen? Dates and times have tons of different formats and there's time zones and there's all these different complex presentations of dates and times. These are unique because you'll have numbers, you'll have slashes, dashes. You'll have like a C for like Zulu time or like EST for Eastern Standard Time. Dates and times are their own unique data format. And the very important because you need to know when something happened. Some types of data. Image data is a very important one in today's world. Images and videos on apps like Snapchat or Facebook or Tik Tok, any social media app, Google, you can do functions now with images that are all based off of image data. So every image has pixels, and every frame and every video has pixels as well. Just like this video, like I'm talking right now, where I just put my hands up for the last couple of seconds that would cause different numbers to occur in those frames with those pixels in these areas where my hands are. So that image data can actually be translated to identify things inside of it, like my face, if you've ever uploaded a picture of yourself in some friends to an app like Facebook, you probably got some sort of notification that you could tag them because it identified your friends inside the photo. And that is all because of image data, very important to understand in today's world. Another use case for image data would be something like reading a signature off of an invoice forum or if a package was signed for pollen delivery, you could get an image to be read by a computer where I could actually figure out if that was signed or not or what amounts were listed on that paper invoice. You can take an image, a photo of that, and then have a machine read that for you. Instead of having a human had to figure that out. Location data. So there are numerical coordinates in a variety of different forms. You usually deal with a latitude and longitude variables. So an x and y decimal numerical representation of where something is in the world. Or you could have a text address which would have numbers and also letters that would make up an address. And this would give you an idea where something was, something like a city name could be text, but then very specific locational data tells you what city something happened in. And then lastly, a datatype called a Boolean. This is a really interesting one and actually is the least. When one of the most and least intuitive data types out there, you can use a Boolean to represent kind of a light switch in data. So Boolean represents either a one or a 0, indicates either a yes or no, up or down on or off. It's a datatype that represents two options. And you can use this to indicate whether or not something has happened is the one. It's a two choice status that helps you filter data. So you can use booleans to figure out, hey, is this person above the age of 65 or no? And then you just put a 0 or a one yes or no. So that about sums of that for most data types. And their use cases. Hope this was helpful for you to understand datatypes. 5. 04 - Applying data types, how do we use them?: So there's two general categories of applications of these data types. And these are quantitative and qualitative. So quantitative, based off of the kinda root word quantity, deals with the measurement or quantity of the data being collected. So speed, temperature, number of seconds. All of these would be quantitative data types. So qualitative, on the other hand, is usually a text variable that indicates a quality of the data being collected. Something like color or size would be a qualitative datatype. And that would be kind of how you would apply that datatype. So a use case for this would be if I sold t-shirts and I had 20 t-shirts that we're all 2000 thread count. I don't I don't know if that's a real thread count. And they were all different colors. So let's say I had ten blue, ten red, and 10 green shirts. And then in-between those three sizes, I had some smalls, mediums, and extra larges in each size. So that would be a use case for quantitative and qualitative data across a data types that I just mentioned. 6. 05 - How can you best keep, organize, and structure data?: So how do you best keep an organized data? So the answer to this is by using something called a data structure. So a data structure is simply that it's just an organization of data in a defined way. The best and most common data structure in the modern world to keep your data within is an electronic database. So a database consists of one or more tables of data. Each table is very simple. It just consists of columns and rows. So you have your vertical and horizontal element and you have pieces of information organized, generally, with each entry into the table being rows and then each column representing a quality or quantity, some sort of individual datatype in some sort of use case represented in each column. In some cases, you might see columns and rows flip to the other way where you see the qualities and quantities and the types of data that you want collected. And in the rows. And then you might see the actual entries represent along the column. But generally you're going to see the other format where the columns representing the qualities and then horizontal, you'll see each entry. So an example of that would be in your bank account, you would have maybe a column for where you spent the money. And then you might have a column for how much money was spent. And then for the rows horizontally, you would have represented each transaction or each day, and then a total sum or a transaction split out. So that'll be a use case and a representation of how data is kept and organized. You probably see every single time you log into your bank account. So I hope this was helpful to help you understand how to keep an organized data. We're going to be identifying some of these later on in the course, some tables and data structures. We're going to be looking at those a little bit more. 7. 06 - How is data used?: So how does data used? There's a couple of different examples that I'll talk through right now that sort of encompass all the other concepts that we just spoke about. So one way that's very common is to track history. So history repeats itself. So if you don't understand it, you're doomed to repeat it. So accounting, write the history of accounting, kinda talking about the bank accounts and bank statements. If we keep all of our data into fine structure in a table or multiple tables, and then each row is a entry of accounting. How much did we spend, how much did we make in our business that will help you track the history. So that later on in my next example, you can track trends. So tracking trends is another barrier you use very powerful use case for data. If you want to see where you are currently in where you're going, you have to keep track of data in a structured format so that, that way you can paint that picture that says, Hey, income is going up, expenses are going down, right? Or vice versa, and then, you know, you're in trouble and you need to make some adjustments to your business. So that example right there just shows how useful it is to attract data and store it in a structured way. Just when we're speaking about accounting data, which is something that hopefully every business is tracking. So one use case for trends is, let's say that we've been tracking our data in our organization. Let's say we sell shoes. And we know that over the past four years, every Christmas season from December to January, our sales have always doubled. So right now we're in the fall and we're thinking how many issues that we need in order for this Christmas season? Well, we can look at our current sales numbers and we can see, and we're going to need to buy double because we know that that is the pattern that always exhibits itself every year for the past four years. And we can't tell the future, but we can make an educated guess or a more educated guess by storing, tracking, visualizing our data so that we can get those indicators of what decisions to make in our business. In other use case, which kind of rolls right into the other ones that I just spoke about is that data can be used to provide instruction or direction. Kind of just like with the shoe sales, I like to use a very simple explanation for data and data visualization that you may not be thinking about. So on Thanksgiving, most people cook a turkey. Now a turkey thermometer you put in the turkey and it will read for you with data visualization and usually a little needle, it'll say, here's the temperature of your turkey. And generally you want that Turkey to be, I'll just say, I don't know, really know off the top my head but let's say 300 degrees. So if you look at that needle and you see that the turkey is only at 250 degrees. You know that, that turkey needs to stand there for two more hours. You have the direction and you can be instructed by the data that you have available to make that decision. And a tricky thermometer, and no one really thinks of it as a data visualization, but in a way it is because let's say that you didn't have any numbers on that dial. You didn't have any representation of what that needle meant, then you would I don't know what degree is it just kind of halfway up the middle. I don't know that that means 300 degrees in the middle. So they had to put something on there, those numbers with that needle to visualize that data coming off of that sensor at thermometer that's inside the turkey so you can make a decision. It's kind of a unique and probably overlook use case for data and data visualization that I think is kind of funny that rules into the examples that we're talking about here. So think of the data that you have available now that we've talked about all the different types and all the different use cases. And think of data that you have floating around in your business and how you could use it for the better to make your business better and to make your lives easier. 8. 07 - How is data used? pt. 2: So let's talk a little bit about how to employ data. So I have the data of Gottingen annotated table. I've got to end database. So now what? So data is most useful when you can interact with it and analyze it. So this is where we get the term data analysis. So data analysis just means that we're finding the answers in the data. And you could find the answers in data in just the raw table of columns and rows and just look through that and somehow just kind of remember things and sort of establish a pattern just in your own brain, are writing down some notes, but that could be pretty painful. So that's why we do data visualization. So data visualization just means that we're making the data look pretty and useful so that we can find more answers. So data visualization aids in our data analysis. So we can do better analysis of the data because we have taken that data and made it usable to just the average person. They can just look at it and they can see a number kind of like what we talked about with the turkey thermometer. You had that gauge there with that needle and you have all the degree is listed there. So that way you know exactly where you're at, exactly where you need to go. 9. 08 - How do we capture and obtain new data?: A really important concept is how we obtain new data. So data comes from either a human or a machine somewhere. It comes from a sensor that's reading temperature in a turkey or outside or rain and a river. And like pH level inside a big garden soil. It could come from a machine in that way or it could come from a paper form the humanists filling out, submitting to you, or a web application where someone is using your website or your web application and submitting things to you like a contact request on your website. Were. And email is even just a piece of data that you're capturing through your interface of just, I use Gmail, just your Gmail. So you can connect to sensors and you can connect to databases online. So you can use little pieces of code that do what's called an API requests to get these pieces of data. And these datas are spread about all over on the Internet. Almost everybody has an EPI. And inside of that API you're tapping into databases that are getting a different pieces of data that are either coming from humans or some sort of sensor or machine somewhere. Data doesn't just come from nowhere. It has to be constructed and then made available. And there has to be some forethought into it. So we can capture new data from users by employing web forms, web applications, or making connections to those databases and sensors. So we can get a hold of the data that we need for our own purposes. 10. 09 - Ways data is used in businesses and organizations: So a couple of different ways that data is used in businesses and organizations today across a couple different common areas. I'll just talk about a few of these when you think about your own organization and where you might have data, these will probably come up and you can start to think about how you can optimize these and make these data processes and these pieces of data work together better. So one of those is very common that I've kind of already talked about is accounting and records keeping. So a common way that these databases are kept simply just Microsoft Excel sheets. Some people use other accounting software for their needs so that they can have it all available in a digital sense of they can automatically file their taxes through it. And there's an endless amount of different applications in ways to store that data, but that's a very common area, that data is utilized unstructured. Another one is payroll in Human Resources. So pay rates, time cards, your hours worked, the length of time you've been employed a company. These are all very common data points that you're going to see in any HR department, in any organization or any company. Talent management and hiring is another common one. So positions that are available, current employee qualifications, potential employees or leads that a recruiter has or hiring manager. These are all data points that those departments will keep track of and that's a very common one as well. Seals numbers and customer data. So this is a gigantic one. Companies generally aren't going to want to take hold of any customer data they can get. So your age, where you live, what other products you've bought, your opinions, your thoughts, as much as they can get, if they could get your thoughts, many companies would want to get that because that would help them better sell to you. So data about who is bought products and services and how much they paid, That's an extreme interest. One very common use cases just in your accounting when you're checking out the money coming in, keeping track of who that came from. Some data points like their email and their phone number might be helpful so that maybe after a while it can reach back out to them and see if they like your product or service. Again. Project management is another common one. Data behind what projects that you're working in an organization, who is assigned to them, how long they should take and how long they do take. Project management data, this just plays a huge part in a successful organization. And more and more organizations are thinking about this datatype in their employment, in their workplace, they can be more effective. And it all comes down to the data and tracking it, structuring it and keeping it in an organized format and employing it for the benefit of completing projects more efficiently and faster. At the end of the day. 11. 10 - How to build your course project: All right, So you made it to the end of the course. Now you just have to build out your project. So I'm going to give you this short video here to explain what you should use and what she put in your project, how that all works. I built mine in four slides and you can use PowerPoint if you would like. But if you want a free alternative, Google Docs has Google Slides. If you just Google, Google search, Google slides, you'll be able to find that and a Google account is free. You can just sign up. You can get into the free software and you can build out the slides just like I did. I didn't do any crazy design on them or anything. I just added information. So the information that I added is I've just highlighted a use case that I found to be a useful leveraging of data. And I've just highlighted a couple of different aspects of it. So the first slide, you can just put in just a cover and you know what your, which are dealing with, what your use cases that you're highlighting. And then on the second slide what I did was I just highlighted how the use case works. So the use case is cryptocurrency market data used for analysis. So the market data is collected via an API request. Then the data is stored in a Cloud database. So that's the structure. So we know how we got it, where it came from, how we captured it, where it's stored. And then I have then the data is visualized through Google Data Studio in a dashboard. And then those dashboards provide tools that allow for better analysis of cryptocurrency for traders of the coins, coin or coins. I should add that in there. Then I just added some screenshots of what I'm talking about. You don't have to do this, but it is kinda nice to just have some visuals. So right here, this is the API request in the database where I'm storing the data. I'm through Google Sheets and I'm conducting the API request right here. You don't have to have an example of this crazy. This is just a product that I had a use case for data that I saw. And then right here it's just kinda behind. This is just a data visualization that I made for a cryptocurrency. And just the summary, hey, key elements, this is what these slides highlight. The highlight the capture the structure of the application of data and what type of data was used. And the type of data in a sort of implied here we got lots of numbers, dollar figures, percentage increases. And then we also have the, the coin names themselves, right? But something simple like this, we just highlighted a use case and just use all of the information in the course. Sort of make your decisions on how to structure and articulate this. It doesn't have to be four slides. I think that should probably is a good minimum goal to hit, but you can make it with more explanations as you like. And the way that I export or I save this PowerPoint and then I can upload that to Skillshare is just hit File. And then you can say download. The download is Microsoft PowerPoint. So really easy process to get this project up. So good luck with your course project. I hope that this course was helpful. If you have any questions, please throw them in the comments and also, please leave a review of this course so that I can help me future courses better or make this course better for you with some sort of revision. If you think anything is off, please let me know. We'll just love to make courses better for you here on Skillshare. So thanks for hanging out with me. Have a good one and look forward to seeing your course projects.