- 8 hours ago
Statistics is one of the most useful tools in data work, and this introduction explains why it matters across analytics, data science, and data engineering. The lesson starts with raw data and shows why it usually needs to be cleaned, transformed, and moved through staging, development, QA, and production before it becomes truly usable. It also breaks down the core tools data professionals rely on, from spreadsheets and SQL to Python, R, BI tools like Tableau and Power BI, and dedicated statistical software.
Along the way, the video lays out the foundation for the series ahead: mean, median, mode, spread, standard deviation, and sampling. It emphasizes practical understanding over theory, showing how these concepts are used in real data jobs and why it helps to calculate them by hand before relying on built-in functions. If you are learning statistics for data analysis, this is a clear starting point for building confidence with the basics.
Created for viewers searching for statistics for data analysis, intro to statistics, data analytics basics, Python for statistics, SQL and statistics, and practical statistics for data professionals. It is also useful for beginners who want a hands-on statistics tutorial, a data science foundation, or a simple explanation of mean, median, mode, standard deviation, and sampling in the data world.
Along the way, the video lays out the foundation for the series ahead: mean, median, mode, spread, standard deviation, and sampling. It emphasizes practical understanding over theory, showing how these concepts are used in real data jobs and why it helps to calculate them by hand before relying on built-in functions. If you are learning statistics for data analysis, this is a clear starting point for building confidence with the basics.
Created for viewers searching for statistics for data analysis, intro to statistics, data analytics basics, Python for statistics, SQL and statistics, and practical statistics for data professionals. It is also useful for beginners who want a hands-on statistics tutorial, a data science foundation, or a simple explanation of mean, median, mode, standard deviation, and sampling in the data world.
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00:00What's going on everybody? Welcome back to another video. Today we're going to be learning all about
00:04statistics. Now using statistics is really relevant in just about any data job. I've worked
00:15as a data analyst for many years but I also worked with data science people and data engineering
00:19and there was a lot of statistics concepts that I've learned throughout the years. And so it's
00:23not just data analysts who use statistics, there's lots of other jobs and roles that use statistics
00:27as well. Now we are about to dive in into an entire series on statistics and that's going to be
00:32five
00:32or six videos. We're going to start with really basic concepts like mean, median, and mode but we'll
00:37work our way up to things like sampling data and even understanding confidence intervals. Now full
00:42disclosure I don't have a background in mathematics. Everything that we're about to learn I mostly just
00:47learned hands-on as a data analyst over the past seven or eight years. I actually have a degree in
00:51recreational therapy which again has absolutely nothing to do with mathematics but when I was in
00:57high school I took a lot of AP Calc classes and trigonometry and so I was good at math even
01:02before I became a data analyst. I loved math that's just not the career I had originally chosen but as
01:07you know I switched careers to become a data analyst and now I've been working as a data analyst and
01:11an
01:11analytics consultant for almost 10 years. So with that being said if you're looking for something that's
01:16like academic there are going to be lots of other statistics channels on here. This is for more
01:20hands-on practicality of using statistics within the data world. Now before we dive into this first
01:26lesson I want to let you know that I have an entire statistics course on analyst builder which
01:30you can go and check out in the description. It covers everything we're going to talk about in this
01:34series and it goes really in-depth and really hands-on and covers a few topics that we're not
01:39going to discuss here on YouTube. I'll have a discount code if you want to check that out in
01:43the description but without further ado let's jump into our first lesson. Now what we're going to talk
01:47about in this first lesson is just why we care about statistics, what it is, how we use it as
01:53data
01:53professionals and some of the core concepts that we're going to be taking a look at and that I
01:57really think that you should understand and know especially as a data professional. Now when we're
02:02working with data we work with a lot of raw data and raw data is just data as it sits
02:07within the
02:07system you're using it or however you're collecting it. It hasn't been cleaned it hasn't been transformed
02:12and you haven't applied statistical concepts to it to actually make use of that data. Now when I'm working
02:18with data and this is kind of just a side note but I still wanted to discuss this but when
02:22I'm
02:22working with real data I am almost never going to use the raw data. Typically this is how it looks.
02:28We start with our raw or staging database on the left hand side. This is just where we import the
02:34data from whatever source we're getting it from. It could be an API, it could be another database,
02:38it could be flat files or CSV files or whatever it is and we just bring it into our database
02:42but then
02:43we have to start cleaning it and we have to start you know digging into that data and once we
02:48do that
02:48we're putting it into our development database. Once we validate that maybe we have a QA analyst
02:53look at this then it'll move into our production database. This is where we start connecting it to
02:58a lot of our different systems and tools. So here we might start building dashboards off this production
03:03data. We might start bringing this data into different tools that we are going to look at in
03:07just a little bit. But this process is really important because typically you're not just going to be
03:11using that raw data. That data is just typically not super usable for your end statistics that you are going
03:17to be
03:17using. So now let's talk about how different data roles use statistics and this is just from my
03:23experience. You could use some other tool that I've just never heard of or some third-party tool
03:28or even certain domains have tools that are really common in that domain like maybe in banking
03:34that I just personally haven't used. So this is just kind of in general. Now the first one is probably
03:38the most obvious and that's just in spreadsheets and you can absolutely use statistical concepts and a
03:44lot of just generalized statistics within a spreadsheet just within Excel or Google Sheets or whatever it is and I've
03:50done that a ton. But what I will say is that Excel kind of limits some of the things that
03:55you're able to do
03:56within a spreadsheet. Now that has been somewhat updated with the ability to add Python within something like Excel
04:04because you can do that now. But again, I wouldn't say it's extremely common to use more advanced statistical
04:10concepts within spreadsheets. Now another tool that you're probably going to use is SQL. And SQL is how
04:16you're going to query your data. Now again, you can do this in a spreadsheet, but you do it in
04:20a different
04:21way and a SQL database is going to be able to hold an immense amount of data compared to a
04:26spreadsheet
04:26which has a limited amount. Now the two that I really want to focus on while we're here is Python
04:32and R.
04:32Now R is literally built by statisticians for data modeling for statistical modeling and for research and I have
04:41personally used it many years when I was on a data science team and there are so many things that
04:46you can do
04:47especially stats wise within R that you just can't do within something like a spreadsheet or SQL or BI tool.
04:53Now Python
04:54has a lot of similar abilities as R, although it wasn't specifically designed for it like R was.
05:02I myself prefer Python and I use Python. And in these next several lessons, once we start diving into it
05:09and
05:09getting more hands-on, we are going to be using Python because I could use a spreadsheet to kind of
05:16explain
05:16some of these things, but we're not going to be able to get into more of the advanced things. And
05:20that's what
05:21I want to dive into. And so Python and R are probably two of the best options if you want
05:26to really dive into
05:28statistics within the data world. You of course can use a BI tool like Tableau, Power BI, Looker, just
05:33common BI tools that a lot of companies use. These are more for kind of the end product of your
05:39statistics. You're typically going to have your data in SQL and you're going to be, you know, using it and
05:45getting it ready and cleaning it up. And then you'll read in that data with something like R or Python
05:49or
05:50wherever that data sits. You can read that data into, you know, these programming languages and do a lot
05:55more programming heavy statistics on that data. And then within those programming languages,
06:00if you want to visualize that, that's great. But to share your statistics with the world or with
06:06other people, your company, sometimes they don't understand Python and R. You have to simplify it a
06:11little bit. And that's where something like Tableau or Power BI would come in. You would simplify your
06:15findings and you'd put it in there so that you can share your dashboards and other people can then
06:20look at your analysis. Lastly, we have stats software. And this could be anything. I personally
06:25have used SPSS many, many years ago. But there's so many stats software or statistical software
06:31specifically made for stats that you can find out there. And this just depends on your company and
06:37what they may have. But these are a lot of the tools that you will use to use statistics and
06:43really
06:43dive into the data and then share that data with the world. Now, these are some of the core concepts
06:49or
06:49things that, you know, you should know ahead of time before we start this series. We'll learn how
06:54to calculate a lot of this by hand because I think understanding how to calculate it yourself instead
06:59of just using the function that's built in is actually really important because there's a lot
07:03of nuance depending on your data. Now, with sampling, there's lots of different ways you can sample
07:08data. And we'll take a look at different sampling methods. But we're going to start with the top left.
07:13We have mean, which is just the sum of all values divided by how many there are. This is what's
07:18called
07:18an average. Next, we have the median. And median and average or median and mean often get kind of
07:25confused or messed up. And this is a really important concept because median is the middle
07:30of your data. It's exactly cut in half. And oftentimes people think that's your average,
07:35but they are two totally different things. And once we get into the next lesson, we're going to get
07:39hands-on with mean, median, and mode. That'll be kind of our first hands-on lesson. I'll show you how
07:44these are calculated and why they shouldn't be considered the same thing, even though they get
07:49mixed up a lot. Lastly, we have mode. And that's the value that appears most often in your data
07:54sets. Those are what I would consider just the big three of simple statistics. This is like your basic
07:59level statistics. That's why it's going to be kind of our first lesson or hands-on because you have to
08:04know this to kind of go further. Next, we have something called a spread. And that's just how far apart
08:09your values are. Are they packed really tightly together on something like a bell curve? Or are
08:16they really spread out? And how can you determine that? What statistics can we use to determine that?
08:21And why do we even need to know that? We're going to dive into that in its own lesson. Next,
08:25we have
08:25standard deviation. And this is the average distance each value sits from the average. And standard
08:31deviation is super important and actually affects a lot of things down the line in statistics. Again, a really
08:37important core concept to know. And then sampling is when you look at a smaller group of your data set,
08:44you analyze it, and then you can draw conclusions based off of your entire population. Is it 100%
08:51accurate? Not likely. But if you don't have access to all of your data or an entire population of people,
08:58sampling becomes extremely important. So that is all we have for this introduction to statistics series.
09:05We're going to cover a ton in this series. I love getting super hands-on, making it really practical.
09:11In the next lesson, we're going to dive into that beginning part, which is the mean, median,
09:15and mode. This is something that once you really understand it, you'll be able to apply it and
09:19really just know what you're doing when you're working with your data for basically the rest of
09:24your career, which is a huge, huge thing. So thank you guys so much for watching. If you like this
09:29video, be sure to like, and subscribe, and I will see you in the next lesson.