And welcome to my mini course on the essentials of data science.

This mini course provides a super basic looking to data science what it is and the three main components that make up data science.

Data Science is a very mainstream word, like it's thrown around a lot, but it's actual definition is quite big.

This many courses designed to help those of you who are curious about data signs develop a better and more specific understanding of the topic.

They're definitely more advanced techniques within data science such as machine learning.

But even these can be traced back to the three essential components that will cover before we get straight into it.

I thought I quickly introduced myself.

My name is Max and I work as a data scientist.

After getting my degree in physics, I find myself more and more drawn into the world of data signs.

So instead of diving into the realm of physics research, I taught myself all the tools and techniques a data scientist needs and shortly after landed my Dream Data Science job.

I've since also started teaching data signs to others and have been fortunate enough to teach what is currently over 9000 students.

The scales off, gathered and learned over the past five years of my data signed Stern, so let's jump right into it.

So what is data science?

Well, data science is kind of summarize it in different ways, but the main parts of it are transforming data into information.

And this is a really big step because a lot of people talk about, you know, data and Big Date and all of these things.

But data by itself isn't really that useful until you can turn it into information.

And so if you just have a bunch of numbers appearing somewhere, and it's just, you know, so much of it.

No one can make sense of that.

And that's where you need a data scientist to be able to transform all of these.

All of this vagueness and kind of this noise, too.

That's going on, and you need to be able to extract information from it.

And that's what a data scientists does.

Now what you do with this, too, with this information or how you get this information, it's through analyzing their data, So a big part of it would be, you know, cleaning things up, doing some some processes on it.

And then you analyze once you've cleaned things up a little bit, and that is one of the ways that you can then get information out of your data.

Um, through this analysis, and you can kind of continue on and you see trends and patterns and all types of correlations, hopefully, on all of these things again build up into this turning data into information component.

Um, and then ultimately you also need to contextualize everything that you have.

So your computer can't do that for.

Your computer can kind of crunch the numbers and stuff.

But it's your responsibility also to make sense, what's in front of you.

And even if you see something, you just don't blindly trust it.

But you need to understand, you know, where am I at?

Where am I coming from?

Where is this data coming from?

Immediate.

Be able to contextualized these things and then, of course, be able to apply as well as understand them.

And so once you have this data, you know it's great, but turning it into an information into great information that you can use and directly apply That's where the rial power lies.

And that's also kind of the role of a data scientist.

So that's what the data, that's what data signs pretty much is.

And so what is the data scientists do?

Well, we kind of already talked about this just a little bit, but let's go over it again.

Any more concrete examples?

And so the data scientist would, for example, get and process this raw data and then converted into something a little bit clear.

So you can imagine kind of just like a data stream coming in.

And it's you have this measuring device and constantly is just measuring all sorts of data and because, like, nothing is really Constance, everything will be fluctuating.

I've been down, and so a date assigned to this would be the head of take all of this data.

It be that kind of clean it up a little bit, you know, maybe reduce this fluctuation that, you know isn't supposed to be there.

That's just kind of background stuff going on and then put it into a format so that you can easily plotted against some things on, and then we already get to the next point that, you know, once the state as cleaner, you can maybe do start doing some calculations on them figuring out the core statistical components, you know, like, what is the average values of these?

What?

What am I really dealing with, You know, getting a first look at first understanding of what it actually is that you're tackling.

And then once you have this kind of understanding that you can start to do some visualizations which help you as a data scientist, maybe see some trends or patterns already.

But visualization is also really key because they let you show it to other people.

And they're a great means of communication.

So they help both US data scientist as well as helping others when you try to convey this information to them, all right, And then finally, you have to suggest some applications of the information, so it's not really enough to just be able to look at it and say, like, Yeah, I see it goes up and down, and that's that's good.

But what does that mean?

How does this transfer into something useful?

And that's also one of the key roles of a data scientist transferring information into knowledge.

And so you've got this data into information step.

But you also need to transfer this information into knowledge and those air to really powerful things that are worth a lot a lot.

Um, and that's pretty much what a data scientists focusing on.

And then you can go further, you know, and take this data and do machine learning with it or something.

If you really understand what's going on or if you have some hypothesis of, you know what could happen so you can take things a lot further.

But ultimately this kind of turning data into information and then into knowledge, that's kind of your role.

All right, so let's go into the essential techniques or the essential components of data science.

So the first essential component we kind of touched on this already is statistics, and basically we're gonna cover this later on.

But let's just give ah kind of quick wrap down.

So in statistics, you need to understand different data types that you can encounter.

And so there are day I can come in different ways and we'll go again into more detail with this later.

But it's not just you know you get a bunch of numbers that I can come and very many different ways depending on the field that you're in.

And so you need to be prepared and you need to kind of be aware that data may not always just be a direct number for you.

And then, of course, you need to understand some key statistical terms, like you know, the different types of means and also understanding, fluctuations and data.

And the reason this is important is because these key statistical terms give you an overview of how this data is behaving.

And depending on how the data is behaving, you may want to approach it differently.

So if you know that your data is very clean, there's very little fluctuation.

Then if you visualize things, you can probably trust what's going on or if you want to maybe fit some curves to it or something.

But if you see there's a lot of fluctuation in your data, visualizing it is gonna be much more difficult because you just see jumps everywhere and you're not really sure which of this is actually true and which, if this is caused by you know, like some interference somewhere or someone is messed with my system.

And so all of these things will kind of be hinted to you through statistical terms.

So it's probably good that, you know, you're kind of comfortable with these things and that you can be able to get some meaning meaning out of them.

All right, on, then, finally it be and statistics to be able to, you know, split up on group or segment data points so that when you have this big data set, you wanna be ableto maybe split it up into smaller things, compare different regions, look more into more detail into some things and maybe, you know, isolate two components because, you know, hey, these things are probably gonna be important.

The rest I don't really care about that much.

So being able to kind of important isolate and meddle with the data a little bit.

So these are the kind of statistical components that we're gonna look into it.

All right, so the next big thing and we've already talked about this too, is data visualization.

Andi, we'll see why data visualization is a really key skill for data scientists.

And then we're also be gonna be covering different types of grass that you can use and how you can compare different number of variables.

So, for example, you can have one variable grass where you only look at one thing and you only want to look at this and you want to see how these how this changes, you have your typical to variable grouse, which you probably know where you have this X and a Y axis.

And then you can kind of see how two variables relate to each other, where you can have three variable or even higher variable graphs and where you plot maybe three different things or even more if you want, as long as it makes sense next to each other, so that you can compare multiple things at the same time, all right.

And now we come to the other big thing that you're probably gonna need as a data scientist, which is gonna be the ability to program now, not every data scientists can do this, but this is really, really essential, in my opinion, to your role as a data scientist, because knowing how the program is gonna make your life so much easier if you know how to program.

You can kind of take your ideas and your thoughts, and you can put them into actions in the computer.

And you can just automate everything you can customize things you can explore.

You can prototype, you contest, and you're not reliant on some you know, application.

You don't have to master some application.

And if it doesn't work, if one feature isn't there, you have to contact customer support.

And maybe it's not even possible.

And then you have to wait for an update.

Or maybe something is bugged with programming.

There's just you're so much more reliant on yourself, and you can really just do whatever it is you want to do.

And you're not reliant on other people or on the tools that other people have built for you.

But rather, you can just pretty much go and, you know, just do what you want to do without there being major roadblocks on Ben, we'll also look at some essential packages and python.

So in programming, you never want to reinvent the wheel.

You always want to start off with the last person left off, and so the ability to program and be able to write simple programs you would need to teach yourself, but you wouldn't need to write highly complex mathematical packages or data analysis packages.

Those are already out there.

All you need to do is be able to download them and implement them in your coat, and they're gonna work.

You know, they've been tested a lot.

There's a huge communities working on them on improving them and everything.

All of this is for the community.

And so the whole community kind of works together to improve it.

No one's really directly trying to make a lot of money off of it, so they're not gonna charge you all of these service fees and everything.

Everyone's just trying to improve their package because if it improves, everyone also benefits from it.

And so we'll look at some of the libraries.

We'll talk about some libraries that you can use, especially in python, and to help you along your way with data analysis and to become a successful data scientist.

In this chapter, we're gonna talk about statistical data types.

Now we're gonna look at the three different types of data which are summarized as a new miracle, categorical and orginal types of data.

Now, these are the types of data that we talked about before.

How you can't just expect your data to be be kind of new miracle.

And so we'll see numerical data.

But we'll also see the two other types of data that you may be.

You know, encountering in your career is a data scientist.

All right, so let's talk about numerical data.

First, though, numerical data is also known as quantitative data on, and it's pretty much things that you can kind of measure.

It's it's great numerical stuff that you can do math with.

You can compare it, you know, saying this.

Plus this makes sense that he is greater than be, um, these air.

You know, all examples of numerical data, numerical data.

Can we split up into two different segments?

One of them is going to be discreet, and so discreet means the values only take on distinct numbers.

And an example of this would be, you know, um, like you or something like that.

A measurement of like you, or if you do a coin toss the number of times that you toss heads so you can you know, you can have 15 heads.

You can have 12 heads out of, you know, 20 coin tosses.

You can have 500 heads out of 1000 coin tosses or 500 out of 600 or all of these things.

But all of these air distinct numbers and now they don't have to be whole specifically, but they do have to be distinct.

So that's that's the kind of very important part that, you know, there is a kind of step size that you're dealing with.

And, of course, you can still say Hey, you know, flipping eight heads out of 20 is better than fiddling seven heads out of 20 if you want to flip heads that this, um, or flipping eight out of 20 is worse than flipping seven out of 20 if you're going for us, many tales as you can.

So all of these kind of comparisons that makes sense.

So that's a discreet part of numerical data.

Then we have the continuous part, and now the continuous part is really that values could just take on any number, and they're not in limited by decimal place, So a value that can you know it could be like 1.1, and then the next value would be 1.2.

That's not continuous.

That's still discreet because you have this step size of 0.1 continuous means literally.

Every number from start to finish can be taken on.

And this doesn't mean that every possible number in the universe from negative infinity, plus infinity and all imaginary numbers and everything that comes with it that doesn't that that's not required for continues.

It could really be that just every number between zero and one can be taken on.

So, for example, let's say you have a bottle of water in this bottle of water can hold one leader.

Now, if you fill your bottle up and starts off empty and you fill it all the way up to the top, the amount of water that you've had needed to take on every single number between zero and one because you can't just fill up water, you know, a kind of small increments of say, Hey, I'm gonna put in 0.2 leaders every single time because the water doesn't just, you know, teleport from A to B.