2026 FREE Data Analyst Bootcamp [24 Hours+] for FREE | SQL, Excel, Python, Power BI, GitHub, AWS

0:00 Hello everybody and welcome to the 2026 data analyst boot camp. Back in 2024, I released the first ever data analyst boot camp on YouTube. It was about 24 or 25 hours worth of content that I've been creating for the past four, five, or even 6 years. Believe it or not, since then, I've been creating even more content. And so, I'm adding a lot of those new things into this boot camp to make it even longer and cover even more things. In the original boot camp, we covered a ton of stuff already. And I'm going to read it because it's too many things. Uh we covered my SQL, Excel, Tableau PowerBI Python Pandas Python projects, building a portfolio website, creating a resume, practicing for technical interviews, Azure, AWS, and then how to use LinkedIn to land a job. That is a ton of stuff. And those are just videos I've been making for the past several years, and I put it into

0:45 one really long video. In the boot camp that you're watching right now, we are adding a ton of new content. The first thing being a data fundamentals playlist. These videos cover things like what is data, what are data types, what is data cleaning. A lot of things that data analysts need to know. We're also going to be adding in our entire Git and GitHub series because Git and GitHub are just really useful to know and they're being used more and more especially with AI becoming so popular. I'm also adding in the R for data analyst series which is basically just R programming for data analysts. And so if you like R, you are in luck. I am adding it to this boot camp. Lastly, we're going to be adding in data bicks. And data bicks is a phenomenal data platform to know how to use. It's super popular and a lot of people say you either choose data bricks or snowflake and I'm going to create a snowflake series in the future, but we're adding in the data bricks series

1:31 and so that's going to cover how to use data bricks and how to create ETL pipelines within data bricks as well. So there's a ton of new things that we're adding to this boot camp and it all is here just to help you learn the skills that you need in order to become a data analyst. If you did not know, I also have a paid platform called Analyst Builder. Analyst Builder is where I have all of my full courses. It is my paid platform and so all the things that we're covering within this boot camp, I have full courses that dive even more in depth and have more advanced projects within Analyst Builder. We also have a part of the platform where you can practice for interviews. So you can practice SQL and Python and R all within the platform. And there's also general technical interview questions. If you don't know how to answer some of these questions, we have an entire part of the platform where you can practice and get better at answering those questions. And

2:17 if you are looking for projects to build, we have a whole section on the platform where you can find projects and you can build them out and share them with others. Analyst Builder is my paid platform. YouTube is where I have all of my free content. So, they're two separate things. If you just want to stick to the free content, this boot camp is the best boot camp you are going to find on YouTube. Hands down. I promise. But if you're wanting to go even more in depth and you're willing to spend a little bit of money, Analyst Builder is where I would go. I truly hope that you learn a ton from this boot camp. I have spent years creating the content to put into this boot camp and I hope that you really enjoy it. I have no idea how long this boot camp is going to be. The last one was like 25 hours. So, I hope you enjoy the somewhere between 25 and 30ome hours worth of learning. We're going to start at the very beginning assuming you haven't started

3:03 this process at all of becoming a data analyst. If you already have, you can kind of find and identify where you are in this process and then go from there. Now, before we dive into everything, I want to warn you I will be mentioning my own channel a lot in this video. I have videos and playlists on just about every single topic that we're going to be talking about today. I'll have all the links to those videos in the description so you can dive into those topics more in depth. So, I hope that's okay. And it's all completely free. I've been building this out for the past 3 years and honestly, you can probably get 90% of the way to learning everything you need for data analytics just on my channel. So, now that I've warned you, let's jump to number one and that is learn the data analyst skills. Now, there are literally a hundred different things that you can learn for data analytics. You can learn things like AlterX or a cloud platform or different programming languages, but there are

3:48 some core skills that I recommend you start out with before kind of branching into some of those other skills. The number one skill that I always recommend people start with is SQL. SQL is just one of those fundamental skills I think everybody should learn. Even if you don't use SQL, you'll use some variation of SQL if your company has a large enough data set. SQL is used to actually query and retrieve data from a database. So, if your company collects data, which every company does, they're going to put it somewhere to store. It's usually stored in a database, and SQL is how you get that data from the database. I think SQL is also fairly easy to learn, which makes it really good when you're just starting out. I have several playlists dedicated to SQL, starting from beginner all the way to advanced, and you can learn all of that for free. One other reason why I think you should learn SQL first, is that a lot of companies interview or have a technical interview

4:34 during the interview process on SQL. That's something that really caught me off guard when I was first starting out because I thought it was going to be more behavioral. I didn't even know what a technical interview was. So, knowing SQL actually became a really important part of interviewing and getting a job as a data analyst. The second skill that I will learn is a business intelligence tool like Tableau or PowerBI. Now, there are a ton of different BI tools. I can literally name 10 off the top of my head that I've used throughout my career. But what I will say is that learning something like Tableau or PowerBI is pretty transferable to almost all those other BI tools. They're all fairly similar in how they do things and how they show and display the data. You most likely won't have a technical interview asking you about Tableau or PowerBI like to build something for them. That usually does not happen. But the combination of SQL where you can query your data and then taking that data to

5:20 build something that is a really really great combination to learn right away. I have entire series on both Tableau and PowerBI with projects on my channel. The third skill that I would learn is Excel. Now most people have used Excel. They know what Excel is and how it's used, but it can be used a little bit differently for a data analyst. For example, in Excel, a lot of people haven't cleaned data in Excel or built charts and graphs using Excel, and those are things that data analysts would probably do. Excel is also just a fundamental skill that every company is going to expect you to know. So, I have an entire playlist dedicated to Excel to actually walk you through how to use it for data analysis. The fourth skill that I recommend you learn is Python. Now, a lot of people will have Python higher up on their list. They only use Python. They don't use SQL or a BI tool. They just do everything in Python. Now,

6:06 Python is a fantastic tool. You can use it to manipulate your data to create data visualizations and a ton more like web scraping and regular expression and 100 different other things. But it can be kind of hard to learn. It took me a long time to really learn the basics very well. That's really the only reason why it is farther back. I feel like SQL and a BI tool are really easy to learn and really pack a big punch. Whereas Python can be quite tough to learn in my experience and you may not use it as often as you would something like SQL or a BI tool. If you're interested in learning Python, I have an entire series dedicated to Python as well as projects that you can build. Again, I warned you there's going to be a lot of self-promotion in this video. I have videos on just about every single one of these topics. The fifth and the last skill that I recommend you learning, and this is the only one that I don't have a

6:53 series on yet, and I will make those, is learning a cloud platform like AWS, Google Cloud Platform, or Azure. There's no denying that these platforms have played a huge impact on how we use data as a whole in the data analyst industry. They can be kind of tough to learn, though, if you aren't using it hands-on in an actual job. I think that learning a cloud platform is already something that most people should start working towards because in the future it's only going to become more prevalent. After you learn all of these skills, the next thing that I recommend you do is actually build projects with those skills. Now, what does building a project actually mean? It means taking a skill and then building something out of it that you can then show a potential employer. For example, if you went through and learned Tableau, you could go and take a data set and you could build a visualization and a dashboard in Tableau and that would be a project.

7:41 With these projects, you can build something called a portfolio. And I usually call it a portfolio website. A portfolio website is a website that you create where you store all of your projects and then you can share that with recruiters and hiring managers so that they can see all of your work. Now, do you absolutely need a portfolio to show employers? No, you don't. But it does help in two different ways. The first thing that it may do is actually help you land the interview. If you have a link on your resume and they click on it, they may see your skills and see your projects and be like, "Man, this person really knows what they're doing. This is exactly what we need." The second reason that I recommend building projects is because most likely during your interview, you're going to get asked questions like, "How have you used SQL? How have you used Tableau?" And if you don't have any experience in that, you're just going to say, "Well, you

8:26 know, I've taken courses to learn it." But with a project, you can be a lot more specific. You'll be able to say, "Well, I actually just built out this project in Tableau. I took the data and cleaned it in Excel and then I put it in Tableau and built out this dashboard and here are the insights that I found from this data set." It's just a much better answer. And as a hiring manager myself, I can tell you that it is definitely beneficial to build out these projects. The next step that I recommend you take in becoming a data analyst is building a data analyst resume. The resume, to say the least, is extremely important. It's what's going to actually allow you to land an interview to potentially get a job. Now, if you were like me when I was first starting out, I had a resume. It just had nothing to do with data analytics. So, how do you make a data analyst resume if you don't have any

9:12 experience as a data analyst? Well, you are asking the perfect questions because the very first things that we talked about are what are going to go on your resume, those skills, and those projects. If you have no experience or degree, like myself who has a recreational therapy degree, if you have no background in this, it can be really daunting to kind of display that you know what you're doing and that a company should hire you. So, what I usually recommend is right beneath your contact at the top, you put your skills and your projects that you built out on your resume. Things like work experience and education should go on your resume as well, but just a little bit lower. You want them to see those things before they see that your last work experience was at Domino's and you have a degree in marine biology. It's just not relevant to data analysis. And if you put those things at the top, they're probably

9:58 going to rule you out right away. The fourth step to become a data analyst is actually applying. You have the skills, you have the projects, you have the resume, now you're ready to start applying for those data analyst jobs. Now, there's a lot of different opinions on how you need to go about applying for data analyst jobs, but I'll give you my take on it, and this has been the most successful for me and my career. The first thing that I want to mention is actually what I would not do, which is just blindly apply on Glass Door, Monster, Zip Recruiter, and all these other platforms to just any data analyst job that you can find. Now, I'm not against this. I think you should do that, but I don't think that's the only thing that you should do because the chances of you getting a call back or actually hearing something back are extremely low. To really increase your chances of becoming a data analyst, I highly, highly, highly recommend working with a recruiter. A recruiter is

10:44 literally someone who is there to help you find a job. Now, when I first started out, I didn't understand what a technical recruiter was at all. I was kind of nervous or scared to work with them. But it's actually pretty simple. A company has a position that they want to fill and they don't want to spend hours and hours and hours to find someone to fill that position. So, they hire a recruiter. a recruiter is going to go out and try to find someone to fill that position, aka you. And so if you go and talk to that recruiter and they have a position that opens up, they will help you get that interview. And then if you get a job, let's say for $50,000, the company is going to pay that recruiter, let's say, 10% of your salary. So they'll give them $5,000. So you don't actually lose or have anything to lose using a recruiter. You can reach out to recruiters in several ways, and I've done every variation, but I'll tell you

11:31 my most successful way, which was using LinkedIn. There are tens of thousands of recruiters on LinkedIn. I made an entire video of how you can reach out to recruiters and what to say to recruiters on LinkedIn to help you land a job. So, be sure to check out that video when you actually get to that point. But, you can also just cold email and cold call these recruiting companies. But to me, it's just not as effective as reaching out directly on LinkedIn. And this is just a bonus one. The last thing that you need to do is accept a job offer. So, in step number four, after you apply to those jobs, you do actually have to go in, interview, and then get a job offer, which you will accept. I just thought I'd mention that just in case that was not super clear. Now, that was a lot of stuff. Let's talk about time frames to actually complete all of these things. Now, doing all of these things from scratch is going to take a while, but

12:16 let's break it down by each step and see how long I generally think it's going to take. Let's start with step number one, which is actually learning the skills. Now, just to be upfront, this one probably is going to take the longest for most people. For most people to learn all of these skills, it's going to take around 3 to four months. Now, if you don't learn a cloud platform and Python, which are the last ones that I recommend, and you just focus on SQL, ABI tool, and Excel, I think you can do that in under 3 months. That is very dependent though on how much time you have to study. That time frame is more for someone who has several hours per day, maybe 3 hours in the end of the night after you go to work. That is someone who has quite a bit of time to dedicate to learning during their week. Of course, that time frame is going to take longer if you don't have as much time to dedicate to learning. Now, let's look at number two, which was creating

13:02 projects and a portfolio of projects. From my experience, when you're first starting out, it takes a lot longer to actually create these projects. It can take one or two weeks per project. I usually recommend people doing three to five projects in their portfolio before they start applying. And since they can take anywhere from 1 to two weeks, you're looking at anywhere from 3 to 6 weeks. The next step was to create a data analyst resume. Now, in my opinion, this one should take the shortest out of every single step here because you're really just kind of reformatting a resume or creating a resume. You're just adding skills, you're adding your projects, and then kind of reformatting it to make it look nice. This should hopefully take under a week, but if you use something like a professional service where they help you build a resume, it can take one to two weeks. The two last steps which kind of go hand inand are step four and five, which is actually applying for jobs and then

13:48 landing a job. Now, this process can take as little as a month, or it can take as long as 6 months or a year. It really depends on how you're applying, where you're applying, and just the kind of luck that you're having with actually landing interviews. I've seen people who have never had any experience land a job within a month of starting to apply. And it's incredible. It's amazing, but it doesn't happen too often. You're usually looking at around 2 to four months on average to land your first data analyst job. If you put all of those together and kind of average everything out, you're looking at around six months total for the entire process. Now, I don't want that to discourage you, okay? 2023 is a long year. You have a lot of time and it doesn't have to take 6 months. You could do it faster. You could do it in 3 months and just prove me wrong. But if you are really focused

14:34 and you are really driven to become a data analyst this year, I know that you can do it. Now, to maybe boost your spirits and make you feel a little bit better, I didn't know any of these things when I first started out. I didn't have anyone telling me kind of a plan on what to do. I had to go out and figure all these things out by myself and it took me almost a year to land my first real data analyst job. So, with all that being said, I hope that this video is helpful. I hope you now have a path on how to become a data analyst this year and that my channel can be a big part of that. What's going on everybody? Welcome back to another video. Today, we're going to be starting our data fundamentals series. Now, in this series, I'm going to be walking through some really core fundamental concepts about data. If you've been on my YouTube channel for any amount of time, you know that we talk a lot about data, specifically

15:20 about how to use tools to work with your data. But up until now, I haven't really dived in and broken down the core concepts of what data is and how it's used in the real world. So, in this series, that's what I aim to do. And in this video, we're going to be starting off with what is data. Let's jump over to my screen and take a look. All right. Right. So, in this lesson, we're going to be talking about what data is. Again, we're starting from the very basics. We're working our way up through this series. So, by definition, data is just raw facts and figures. It doesn't even necessarily have to be on a computer. It could be on a notepad. If you're writing down 1 2 3 4 5, that is data that you're writing down. That's going to be harder to use that data necessarily because, you know, on a computer, it's easier to process and use data. But that is data. Some examples of data are things like a

16:07 number, the number 45, that is a piece of data. A word could be data. Just the word completed or a sentence that is also data. And then a date could be data as well. So we have 1210 of 2024. This is a piece of data. Now the thing about data is is that data is everywhere. It's in everything we do all the time. But without collecting it, without context of what this data actually means, it's basically useless. Look at some examples of how data is used in the real world. Something that a lot of you will use almost every single day. So, let's take a look at this first one. This is a weather app. I use my weather app almost all the time. I got to send my kids to school and I want to check if it's rainy, if it's hot, if it's cold, if they need a jacket, if they don't. And

16:52 that is something that requires a lot of data. Meteorologists use tools to collect data on all of these things. Then they're presented in these apps. So they collect data about weather, temperature humidity location time and they take all of this and they aggregate it. That just means they bring all of it together into one place and they aggregate these numbers. And finally, they'll present it to you based off of your data, your location, where you actually are. And so that is something that is extremely data dependent. They typically will process this as well as forecast, which means they're going to kind of predict out what they think is going to happen in the next hour, two hours, maybe it's a day or several days ahead. They can also use this data to predict what might happen in your location. Another example would be a bank app. So, I go on my

17:38 phone all the time to check my banking statements to make sure that, you know, I didn't spend too much money. And everything in your banking app is data. And so when you go on this app, like you can see in this image down here, you can see where you spent your money, you can see the amount you spent your money, the date and the time that you spent your money. These are all different points of data that the bank has collected. Banks collect hundreds, if not thousands of data points, but really common ones are things like bank deposits and transactions and payments that you've made. All of these things so they can put it in your dashboard so that you can track your money. Now, let's take a look at different types of data because there isn't just one type of data. Data isn't just one thing. Data is really complex and there's a lot of different things to it. So, really briefly, I'm going to touch on structured, semistructured, and

18:23 unstructured data. Structured data is really neat and really easy to visualize and see, as you can see in this uh image right here. That typically refers to something that has columns and rows, something like an Excel file. Everybody's used an Excel file before, and so that is something that is very structured, very easy to kind of visualize and use and understand. The exact opposite side of this, we have unstructured data. Unstructured data is kind of all over the place and it might be a lot harder to use. For example, that could be something like a photograph that you took or maybe a video or an audio file. These are all examples of unstructured data that you can't really put into something like an Excel file for it to be in columns and rows. Then we have something called semistructured data. Semi-structured

19:08 data is closer to structured data than it is unstructured data, but it is more complex than something like an Excel file. For example, semiructured data might be something like a JSON file. Now, we haven't gotten to file formats and file types yet in this series, but when we do, I'll dive into what JSON files are because JSON files store things differently than something like structured data where they nest data in kind of these hierarchies. Let's take a look at the two main ones. Structured versus unstructured data. Structured data is the data that I primarily work with as a data analyst. This is typically going to be something like a row and column in an Excel file or a CSV file or in something like a relational database. A database is just a place where you will store a lot of data and

19:53 all of that data typically connects in some way. So you can work with a lot of it. Structured data is also quantitative versus qualitative like unstructured data. Structured data is numbers based and it's measurable and so you can easily kind of track it and use it. Whereas qualitative could be something like a survey where it might be free text where it's someone saying you know I had a really great experience at this and I really liked this and I liked this. That's a little bit harder to actually put into numbers how much that person liked. But quantitative may be hey how much did you like this on a scale of 1 to 10? And if the person puts a seven that's a very specific answer. Now the amount of data that sits as structured data is significantly less than unstructured data. Structured data is only about 20% of enterprise data

20:38 compared to 80% of unstructured data. And so there's a lot more unstructured data in the world. And so what a lot of people in the data world do is they try to take that unstructured data and they try to make it structured. And so that is a big part of what a lot of data professionals do. Lastly, and we just touched on this, but structured data tends to be things like numbers, dates, strings, which are things like words and text, versus unstructured data, which is things like images, audio, video, and others. That being said, data is everywhere. It is all around us all the time, and especially in this technological world we live in, it is literally in everything we do. But the challenge especially for people in data who are working with data, the challenge is to collect it, to organize and analyze it so that you can then use that

21:25 data. What's going on everybody? Welcome back to another video. Today we're going to be taking a look at KPIs and metrics. Now, if you've worked in the data world, you've probably heard these terms before because they are very popular to throw around and use. but you may not know exactly what they mean. So, in this lesson, we're going to be diving into KPIs and metrics. So, with that being said, let's jump over to my screen. So, let's dive into KPIs and metrics. And let's start off with metrics. So, what exactly is a metric? A metric is something that, by the way, people throw out all the time and they typically get metrics and KPIs confused. And so, we will be really good going through this. I'm going to talk about metrics first and then how metrics relate to KPIs. So

22:12 a metric is any measurement that provides information using data. So if you're taking data and you're tracking that data and you're measuring that data, that is a metric. It can be anything. For example, let's say we have a website and we sell things on this website. It's like an e-commerce platform. A metric that we might have is website visits. How many visits per month do we actually have? And let's say this month we had 15,000 monthly visitors. We also could look at sales numbers. So, we sold 500 units of whatever product we're selling. So, these are core metrics. Now, if you look over on this right hand side, we have this dashboard. This is something that a lot of companies will have. It'll be something to track their metrics or at least visualize their metrics. It could

22:57 be on revenue or margins or all these different things. This is something that when you're working with data, people want to know. They want to be able to measure and track specific data. Now let's see how this compares to a KPI. KPI stands for key performance indicator and this is a specific metric that directly measures progress towards a goal. So every single KPI is going to be a metric but not every metric is going to be a KPI. For example, let's look over on this image on the right hand side. We start with all of our data and then we take that data and we say what metrics do we want to look at within this data? Then we determine what metrics do we actually want to track. Which metrics align with our goals. So for example, in our website that we

23:43 have, we have 15,000 monthly visitors. But let's say our target KPI is going to be those monthly visitors, but our goal is 20,000. And so we have a metric that we're tracking. And now we're saying we want this to be a goal that we achieve. And it's also measurable. For our sales numbers, we have 500 units sold. One of our sales KPI could be, okay, this month we're selling 500, but next month we want to increase our sales by 2% each month going forward. So that could also be a KPI. You may be asking, how do you choose a good KPI? And this is a very good question because I've worked with a lot of different data teams and they just sometimes randomly choose KPIs. They're like, this seems like a good one to achieve or get better at. That may be true, but it may not actually progress

24:28 you towards the goal that you're wanting. And so when you're trying to choose a good KPI, let's say you're tracking these metrics, you're saying I want to progress towards a goal. How do we choose what KPIs we want? The first thing you have to ask is what is your goal? Maybe for our website it was we want to increase sales. That's our number one thing. Then we have to identify which metric actually best tracks our progress towards that goal. On a website there are a ton of different data points that you can do. For example, you can see how long a user is actually on your website. You can see how many times one user gets onto your platform every day. Maybe they get on three or four or five times a day. Those are metrics, but that may not be what your goal actually is to sell your products. And then lastly, you have to say, is this metric actionable? Maybe you can't make people sign onto your website 3, four, five times a day if

25:15 that's what you're wanting to do. That may not be an actionable metric to actually track, but something like the daily users is because maybe you could send out a daily email or you can do some type of marketing to get more people on your platform. These are things that you have control over and that are actionable. So, really quickly, let's just break down key performance indicators versus business metrics. A KPI is going to provide a direct line of action to a business goal. Whereas business metrics don't necessarily contribute directly towards a goal. KPIs are measured actively as a benchmark to a goal. Whereas business metrics can be measured, but they aren't typically checked and monitored. For example, at a previous company I was working with, we had lots of metrics that we kept kind of tabs on, but we only put the key performance indicators or the most important ones into dashboards or maybe

26:00 into reports that we would then provide to our users. Next, KPIs are a set of standards that are vital to the business. Whereas metrics may or may not be vital to the business. Again, could be anything versus KPIs are like the core things that you want to track and get better at. And then lastly, this is something I mentioned before. All KPIs are business metrics, but not all business metrics are KPIs. You specify exactly what metrics you want to be KPIs to help move your business or grow your business, however you want to do that. So, in a nutshell, a metric helps us understand what's happening in our data. And KPIs show us whether we're reaching our goals using our data. I hope that this was helpful. This was not something that I knew when I first started uh working in data. It's something I kind of figured out along the way. But if you are just getting into data, or maybe you

26:46 already are and you don't know what this is, now you know. And now you can go into business meetings and you can go into conversations being a little bit more knowledgeable. What's going on everybody? Welcome back to another video. Today we're going to be taking a look at data types. If you've worked with data at all, you know that data types are very important. And if you haven't, you're in the right place cuz we're going to talk a lot about what data types are and how they are used. So with that being said, let's jump onto my screen and take a look. So let's dive into it. Let's take a look at what data types are. Now data types, really quickly before we jump into the definition, data types are something that if you work with any data in any way, data types do pertain to you. These are things that you need to know. These

27:31 are things that eventually will either help you or hinder you on whatever you're trying to get done. So a data type is an attribute associated with a piece of data that tells a computer system how to interpret its value. Every single programming language, every database has similar but slightly different data types. I've worked with all types of programming languages and lots of different databases and they all act somewhat similarly, but they can be different at times. So knowing the core concepts that we're going to cover in this lesson can be really helpful to kind of traverse the different types of data types in different systems. To get started, there are a ton of different data types. I mean, there's probably 50, 60, 70 different data types. I'm just throwing out random numbers, but they all fall under certain categories. They're usually either strings, which

28:18 are things like free text, numbers, like 1 2 3 or date and time. So, January 1st of 2024 at 6 p.m. These are kind of the core fundamental data types. But under each of these are a lot of other data types as well. Now within string you could have something like a name. John Smith, Emma Johnson. This is very simple data that you can categorize and you can use. Next it could be an address. 123 Main Street, New York, New York. This is something that can determine a location. Then we have something like product categories electronics furniture etc. These product categories are all strings, but then can be used later on in the data process for things like aggregation. Maybe you want to see how many customers are buying electronics at your store versus furniture at your store. You have to collect that data. You do that with string data. Next, we

29:04 have numerical or numbers. This is probably the most simple data type. It's integers. 25 100 - 15. Then we also have decimals. These are both two separate data types. So decimals is like 12.5 99.99. Now within certain systems, especially databases, they'll have something like a big integer for really large numbers. They'll have something like a small integer. They'll also have just a regular integer. Within decimals, they may have something like a double point system or a decimal data type. So within each of these, they may have even subcategories of data types. Next, we have date time. The dates could be something simple like 1210 of 2024. We have a day, a month, and a year or it could be month day year. There are different formats within dates. Then we

29:51 have times or timestamps. So times just indicate the time of the day. Then timestamps are both date and time combined. So the timestamps tend to be very specific. Then we have dates which give a general day and then times that just give a specific time. Timestamps can be very useful, but often times when you're working with it in the real world, you'll break out these two data into separate columns. You have date in one, time in another, and that may allow you to do more advanced things with this data. So understanding what data type your data is is really important because that allows you to work with it and analyze that data in different ways. For example, if we're working with numerical data, we can use that to calculate averages. You can say the average person

30:36 is spending this much on our website per day. That would be an example of how you can use numerical data. Or with string data types or categorical data, you can group it and count on those occurrences. Or you can do any type of aggregation. sum max min average median all these different things. You can see how many people are buying things in our electrical department versus our furniture department. And that is worthwhile data to use. That really is the basics of data types. Now, if you go into specific things like if you go into SQL or if you go into Python or Excel, I have a lot of lessons specifically for data types in those systems and then it gets a little bit more complex. And so if you want to really dive into data types in a specific system, go into any of my playlists, whether it's SQL, Excel, uh PowerBI, Tableau, they all

31:23 have different data types and you can learn a lot more about data types within that specific tool. What's going on everybody? Welcome back to another video. Today we're going to be taking a look at file types. Now, file types or file formats are everywhere. If you've saved any type of file ever to your computer, you had to save that as a specific file type. So, in this lesson, we're going to be diving into exactly what file types and file formats are and how they are used. So, with that being said, let's jump over to my screen and take a look. All right, so let's take a look at what file types are. File types refer to the format in which data is stored in a file. And you're going to see even in the next slide, there are so many different file types out there. There are a lot. And we're going to get into specific file

32:08 types in this video that you can see a lot of the different types of files that are very commonly used. Each file type is designed for a specific purpose and it depends on what kind of data it's holding and how it's going to be used or shared. You really can think of these like a container, whether it's a bowl or a plate or it's a vase. Each one of these containers is designed to hold different things. And that's kind of like what a file type is used for. Now, if you go right now, you look at your file explorer if you're using a Windows machine. If you go to your view and you look at the details, the view is right next to the sort button up there, you can see the different type of file type. And with all this is my actual, you know, file explorer right now. If you go look in my downloads, we have a lot of different stuff. I have MP4 files. I have different file folders that holds different files. I have PGs, I have

32:55 CSVs, I have PDFs, I have all sorts of stuff. To the right of this you can see the size as well. All these different file types hold the data differently and some take a lot more data than others. So that size is how much data is actually stored within that file. Let's take a look at probably the most simple type of data file or data format that you'll see which is a text file. A text file is super simple and just stores data as plain text often used for unformatted or tabular data. This is data that I would work with all the time as a data analyst. We would work with text files and CSV which stands for commaepparated values. This image on the right hand side is being stored right now as a text file. But if I then went and saved it as a CSV, it would separate

33:41 these values based off of the commas. So country, salesperson, order amount, quarter, this data would be separated and all the commas going down on each row would be separated into basically columns and rows. DSV and text files are super common and they can store a lot of data very simply because they're just storing plain text and so it doesn't get super complex and so it's a very popular type of file format. Next we have structured file types. Now these are files that store data in a predefined structure typically rows and columns or in a hierarchal format. One of the most common types is one that I'm sure almost everyone has used. That's an XLSX. That's going to be an Excel file. your typical Excel workbook. And so right here on the right hand side, this is your standard workbook. You're going to have columns and rows and different

34:27 worksheets and you'll be able to do different things with that data in these Excel files. We also have a DB file and this stands for a database file. So oftent times in the data world, if you're working with a customer or a client, they might give you an entire backup of their database and you can go and use that. And within that database, they're going to have columns and rows, especially if it's a relational database. They're going to have columns and rows just like an Excel file, but on a much larger scale. It typically can hold a lot more data. And then, of course, you get more complex things within a database file, like database schemas to connect and bring all that data together. Next, we have semistructured file types. Now, these get a little bit more complex. These file types have a loose structure, often used to store complex data

35:13 relationships. Some common formats for this are JSON and XML files. Now, these can get a lot more complex than just columns and rows because in columns and rows, it's pretty simple. It's pretty straightforward. But something like a JSON file, which we have on the right hand side, you can have data nested within other data, which just means data within data within data. And so, there can be lots of layers to this data. This is another one that's just really popular and really common within data professionals. And they are really great for storing data that's a little bit too complex for something that's really simple like columns and rows. Next, we have unstructured file types. Now, you saw this within my file explorer. These are files that don't follow a specific format. They don't have columns and rows. They're often just raw data or multimedia data. For example, this video that I'm recording right now, I'm recording onto an MP4 file. This image

35:59 on the right of this beautiful little hummingbird is apng or there's actually lots of different formats for images, but PNG is probably the most common one. Lastly, we have big data and specialized file types. So, these file types are designed specifically to handle really largecale data efficiently. One of the most common and one that if you've worked in the data world or you've worked in Azure or maybe even AWS that you might be familiar with, it's something called a parquet file. Paret files are very common, especially if you're working in something like Spark. That's something where I've used it quite a bit or even data bricks where you're bringing in massive amounts of data and you want to do that in a really efficient manner. So, it has all these different properties that they bring in to these paret files that a normal CSV file would never be able to do or

36:45 handle. Now, what file type you choose can be extremely extremely important. And I've made a lot of mistakes over my years as a data analyst storing data in, you know, one file format versus another. And that has caused issues. And so, and so knowing how data actually is collected and used and stored and analyzed and shared, you have to take all these things into account when you're determining what kind of file type you want. Now, in the next lesson, we're going to be looking at data collection. And we're going to address this exact thing because if you store data in a specific way and then you want to put it in a database, it may not be possible if you store it incorrectly. And so, there are a lot of things to consider within just data collection. But this has been our lesson on file types. What's going on everybody? Welcome back to another video. Today we're going to be talking all about data collection.

37:36 Now, data collection is an extremely part of getting data. In fact, I was on a data collection team for over 3 years. I absolutely love data collection and so I'm really excited to talk about this topic. Let's not waste any time. Let's jump on my screen and take a look. Let's take a look at data collection. Now, what exactly is data collection? Just as a definition, data collection is the process of gathering data from different data sources to use in analysis, decision-m and problem solving. A data source means where the data is actually being created. And data is created all around us. It could be in a hospital ehr system. It could be in your bank account. It could be through APIs. It could be through a website or it could be in a CSV file. So why is data collection important? And what I really should say is why is it really important? One, it ensures that you have

38:22 raw material or the raw data needed to make informed decisions. You can then use that data to identify trends, patterns, and opportunities within that data. And it also lays the foundation for data quality. This is something that we're going to cover actually in our next lesson when we look at data cleaning. So, if you collect data poorly or if you do not process that data correctly, that can lead to bad data, which can give you incorrect results and then of course you're going to make bad decisions with bad data. Now, data collection does not just happen. It doesn't just magically appear. This is a very calculated and specific process that needs to happen in order for you to get that data. Let's take just a really quick example. You are actually running an online shop. You have a website. You're selling stuff. On the right, you're selling t-shirts and jackets and other clothes. I'm assuming now you want

39:08 to track how many customers actually put something into their cart. You might be able to compare how many customers put something into their cart versus how many customers actually bought something. And that could be useful information to you. that data just doesn't appear. A data collection system is set up to collect that data and place it into something like a database so it can be analyzed and used. Now these data collection systems can be something that you manually create and we'll talk about that in a little bit or it could be something that you just pay for. So it could be a system that you say, "Hey, I want you to collect this data." They go and do it for you and you don't have to actually do the work, but there is a data collection system in place. Now whether it's you or some system you paid for, all this data is going to be processed via a data pipeline. Now this is a system that's going to automate the movement of data from one place to

39:53 another while often transforming it along the way. Let's take a look at this ETL pipeline. ETL stands for extract, transform and load. And you can see that uh through this process. So on the left hand side we have all these different data sources. It could be different parts of your website or it could be different websites or it could be different locations of the data entirely. We're going to extract that raw data and we're going to put it into a staging area. This staging area is kind of like a temporary hold for your data where then you can work with data professionals like data engineers, database developers, data analysts, data scientists who can then determine how we want to transform that data. Now transforming data is something we'll cover again in the next lesson when we talk about data cleaning. But transforming the data makes that raw data more usable for whatever you're

40:39 trying to use it for. After we transform that data, we're going to load that data into something like a data warehouse, a database, maybe it could even be an Excel file. It could be as simple as that. But then from there, we can use that to analyze our data. And that's really what data collection is in a nutshell. Now, I worked on a data collection team for many years, and I was a data analyst, and I would often work at every single step of this process. I would have to go and talk to the client and see exactly what data they had available and sometimes they didn't even have data available and so they said hey we want to collect this new type of data as part of my job is I would help them understand how to start collecting that data so then we could create these data pipelines to then transfer it into a database to use data collection is not a one-time thing either data collection is always

41:24 happening these ETL pipelines they break or they need new ones or the source data changed on the left hand side of this diagram the source data changed from one part of the website to another. And so you need to go back and you need to fix that data pipeline to correctly collect all the data that you're wanting. And so this is a very active process. This typically doesn't just happen one time and then it's done. It typically happens one time, then you adapt it and you keep changing or maybe fixing it over time. What's going on everybody? Welcome back to another video. Today we're going to be talking all about data cleaning. Now data cleaning is an essential part of working with any data and so this is a very core fundamental concept that anybody who works with data will need to know. As a data analyst I've cleaned

42:09 data in almost any way that you can imagine. So I am very excited to talk about this topic. Let's not waste any time. Let's jump on my screen and take a look. All right. So let's take a look at what data cleaning actually is. Data cleaning is the process of identifying and fixing issues aka dirty data in your data to ensure it's accurate, consistent, and complete. Let's take a look at these patient names on the right hand side really quickly. These are all my names, but just in different versions or variations. This is very dirty. So, we have Alex Freeberg, Alexander Freeberg, Alex F, Alexander F, Alex Freeberg with a U, Alejandro Freeberg. These are all the same person. That's all me. But if you're looking at this in a database, it's going to consider all these people different. And so what we would have to do to clean up this data

42:55 is we would have to make all of these names consistent and the same. Maybe we do that by saying, okay, all these people have the same date of birth. They're all male. They all have the same social security number. But with all of those things, we could say, yes, these are all the same person. We can make all of these patient names Alex Freeberg with an E. That would really clean up this data a lot. Why do we need to clean data at all? What is the reason that we do this? One is to ensure accuracy because you're going to be giving this data to stakeholders or your boss or a customer and they're going to expect that this data is accurate. But if you're giving them bad data, then they may make bad decisions with that data and that's going to come back on you. Next, it improves efficiency. When you are working with clean data, it is 10

43:40 times easier to analyze and use that data in any way you need rather than it being really messy and really difficult to work with. Lastly, it builds trust and stakeholders and customers and clients, they want to be able to trust that what you're giving them, the data that you're using is actually accurate and complete. And so those stakeholders are really going to trust you if that data is clean and accurate and you can find insights with that data. Now, let's take a look at some other examples of dirty data. And these are not all of the examples, but these are many of them. Here in this data, we have columns and rows like an Excel file. We have a name, a phone number, an email address, and an address. In each of these, you can see a lot of mistakes. Take for example in the name column. We have things like punctuation marks and abbreviations. These are the same person, just like we

44:26 looked at in the example with my name. These are all the same person, but each of these records requires a different row of data because it isn't standardized or cleaned. Let's look at phone number. This one shows us missing data. And this is a very common one where you're collecting phone numbers and all of a sudden you just don't have a phone number. So you're missing that data. Now, what are you going to do with that when you're cleaning it? Maybe you need to populate that data. Maybe that is something that you can get from another data source or you know it and you can actually put it in there. That is a way you can clean that data up. Next, we have mixed numbers and letters. It may look like 831262149, but that one is actually an L. And so this is just a classic case of bad data. They should all be in the same format with the phone number, but they're not. Some have dashes, some have commas, some

45:12 don't have anything, and some even have letters. That's very, very messy. Next, we have email address. Now, within this one, they're all somewhat different, right? We all have different email addresses, and again, that's just a standardization, making them all the same issue. But we also have something like a non-printable character. Now, there are specific characters within computer systems that you just shouldn't be putting or really can't put in specific data types. So, if this is a string, that may be a character that you shouldn't have or can't have. That's going to mess up your data.ly in the address section, we have two issues here. We have messy structure and format. So, some are capitalized. So, so some of the data is capitalized versus not capitalized. We also have incomplete data. For example, we have Emily Renzelli Boulevard, but we don't

45:57 have a number. We don't have a city. We don't have a state. They're just incomplete. And that makes it really difficult to work with that data. Now, here we have something called a data cleaning cycle. And I really like this visualization because it shows that data cleaning is not just a one-time thing. It is something that you have to continuously do. And I have and I have experienced this a lot over my years as a data analyst. Once you clean data isn't necessarily perfectly cleaned. You kind of have to go back and clean it as you go. And it's never really perfect. it's just really usable. This cycle typically starts at the very top. So we have importing data. This is just bringing in data from other systems, usually through some data collection system like a data pipeline that we talked about in the data collection lesson last time. Next we have merging data sets. This is combining multiple

46:43 data sources into one data set. Next we have rebuilding missing data. This is where you're handling incomplete data or you're actually filling in that missing data. Next we have standardization and normalization. Standardization is where you're making sure that the data follows a consistent format. So all the date formats are the same. For example, if we go back to this one, we can make sure that the address is all standardized. They all have an address, a city, a state, and maybe a zip code if you need. They all have the same data. Next, we have normalization. This is a little bit different because you're adjusting data to a common scale without distorting the data itself. Next, we have dduplication. And this is where you may have duplicates of data in your columns. For example, maybe we have a 100 columns of Alex Freeberg and we don't need 100

47:28 columns. It's all the exact same data. We would want to remove 99 of those to just keep the one that we actually need. Next, we have what they call verification and enrichment. This is really just data quality. You're going through and you're validating your data, making sure that your data is accurate. Then lastly, you're saving this clean data in the format that you need for your next processes, whether it's analyzing or you're using in some type of product. Now, one thing I want to mention on here is that this cycle is not perfect. In fact, I usually do dduplication earlier on within this cycle, but it really depends on the data itself. Sometimes you're importing, merging, rebuilding, and then going back and getting more data and then importing, merging, and rebuilding. So, this isn't a perfect cycle. This isn't exactly what you should do every time, but these are often the steps that I am

48:13 taking when I'm cleaning data. Now, data cleaning is very specific to different tools, and I have a lot of different videos on how to exactly clean data with full projects on data cleaning in SQL and Tableau and PowerBI and Python and Excel. And so, if you want to learn hands-on how to actually clean data, go check out those videos cuz they are fantastic to learn how to actually clean data. Today, we are going to be starting our MySQL tutorial series. Now, the entire MySQL series will be broken up in three smaller series. We'll have our beginner, our intermediate, and our advanced. This lesson is the very first lesson in the beginner series where we're going to walk through all of the beginner or the basics of MySQL. Today, we're going to be setting everything up. So, we'll be installing

48:58 MySQL and then creating our database that we'll use to actually learn MySQL. Now, before we get started, I wanted to let you know that I created three full MySQL courses over on analystbuilder.com. I built a crash course for MySQL for people who are going to be interviewing or taking technical interviews. I also created a full MySQL course that's going to cover everything from the basics all the way to the intermediate level. And then lastly, I created an advanced course that's going to teach you a lot of the more advanced things that an analyst would typically use. Those courses are going to go really in-depth and they're going to have a lot of practice questions along the way and we'll also have full guided projects in there as well. I'll have links in the description to all of those courses if you want to check those out. Now, without further ado, let's jump on my screen and install my SQL and create our database. All right, so let's get started by downloading MySQL. We're going to come right over here to dev.mmysql.com/downs/installer.

49:48 And I will have that link in the description so you don't have to write all that out, but you should be seeing this page right here. Now, we have to select an operating system. I'm using a Windows machine. And if you aren't, if you're using Linux or Mac or something else, it should populate it for you. But if it doesn't, just select this dropown and select your operating system. Next, we have two different downloads. We can install the MySQL installer community or MySQL installer web community. This one is very small, but then you actually do have to download the installer. It just gets it from the web. This one I'm going to download the actual installer. It's larger, but this is the one I'm going to do. So, I'm going to go ahead and select download. It's going to ask me if I want to log in or create an account, and I don't. I'm going to say no thanks. Just start my download.

50:34 I'm going to save this in this desktop folder. Doesn't really matter where you save it. We're going to save that. And it's going to download. It should be done in just a few seconds. I'm going to go ahead and click on it. And it's going to open it up when it's finished. And we should get the interface or the UI for the actual installation for MySQL. So, here is the MySQL installer. And first thing we need to do is choose a setup type. Now, we're going to keep the developer default unless you really know what you're doing. And you can select the server only, the client only, full, which is literally everything MySQL has to offer, or custom. So, we're going to keep this developer default, just installing the things that we kind of need. So, let's go ahead and select next. And for whatever reason on my computer, it's saying this path already exists. You probably won't get that, but

51:21 I'm just going to go ahead and select next. And then I'll select yes. It keeps doing that. I can't explain why, but it keeps doing that for me even though I've deleted it from my computer completely. U but it just remembers it somewhere in its memory. Now the next thing we need is to check requirements. Now I just have this one. It says I need to download this uh Visual Studio. I'm not going to do that, but on your screen you may have multiple multiple requirements. Typically you're looking at something like this Microsoft Visual C++ redistributable package. What you need to do is download this. All you have to do is click download. Once you download and install that on your computer and then we go back, all of those should be gone. That's the one that I see the most when I'm actually working with these requirements. I had to install it myself when I got this new laptop. So, go ahead

52:08 and install that if you need to. But if yours looks like mine, we don't need this Visual Studio for what we're going to do. We're going to go ahead and select next. It is giving us a prompt that we haven't satisfied all the requirements, but that's okay. We're going to go ahead and select yes as well. Now we're ready to install all of these things. These are all things that MySQL wants you to install. The most important are the server and the workbench, but it does not hurt to have all these other things as well. Some of these connectors are also important. So, we're going to go ahead and execute. This will take just a few minutes. I'll skip ahead uh when they're all done, but this should take just a few minutes and then we'll continue on installing my SQL. So, everything just completed and now we're going to select next. And now we need to actually configure our product. Now really the only one that we actually need to configure is the

52:53 server. The router says we need to configure it and the samples and examples say we need to configure it as well, but really it's just the server.

…