Hello, guys, and welcome back to another class, of course, about data analysis and science, the
complete introduction with the use of.
So in this class, we are going to have a complete introduction to the concept of data to understand
what exactly is data, how it works.
And that is where you guys will have a complete knowledge about this subject.
So let's jump right into it.
So basically, what is data exactly?
So there is the concept of there can be something really, general, your cell phone, for example,
when you click on your cell phone.
This could be considered as data when, for example, you are using, I don't know, your chips where?
Well, there is data there when you were typing something or on your keyboard, it could be there as
well.
Basically, what will help you can see data could be seen as statistical sets or facts that can be used
for analysis.
So basically, let's say, for example, you guys are walking outside and you are making steps.
Those steps could be construed as data because they could be used to know exactly how many miles you
guys are walking each and every day.
Let's let's say, for example, we want to calculate how many steps we need to do per day to be able
to stay healthy.
Well, the steps that you guys do each and every day can be counted to be able to say that, for example,
you guys have walked 1000 steps and you guys are healthy.
Well, we can make an average of everybody who is walking and say that, OK, in general, on average,
each person that walks one thousand steps and there is a person that is healthy, basically not only
steps, but as I said, it could be absolutely anything that can be quantified, but not only quantified
because there are different stereotypes that exist.
We are going to talk about it a bit later.
So principally, we can have numbers.
As you can see here, we can have quantities, informations, measurements, facts, observations and
graphs as well.
But graphs, I see them more as a visualization, too, because they allow us to see the big picture.
So let's say, for example, we're talking about those steps as well as how many steps are making.
So you will be able to visualize how many steps you guys are making per day with the graphs.
Another thing that is very important to understand with data is that it have to fit for really well,
four big things.
First of all, it has to be able to be collected and stored so easily.
All types of data need to be collected and stored so it can be collected and stored.
Well, it's not really the same thing and has to be measured.
So basically the data that you collected, you can be able to measure it so you can be able to measure
it numerically or with any type of measurement.
You should be able to analyze this data once again.
You should be able if we're talking once again about our steps, this can be considered as raw data
data.
So this is really raw data.
And those steps we can analyze them to, let's say, for example, know how many steps someone who is
healthy is walking, but we can analyze it once again.
And instead of having this conclusion, we can calculate, for example, on average how many steps each
person makes.
Then on average, how many persons per day are working at least 30 minutes a day, for example, once
again.
And we can do a lot of analysis with all this.
And finally, your data should be able to be visualized.
So once again, those steps, you can visualize them on a graph or on any other tool that can be used
in data analysis to visualize things, to visualize what, let's say your conclusions.
So in this case, we can visualize our steps on graph.
So let's say one, the person walked this amount of steps.
They do this person walk this and on the steps, et cetera.
So this is for data.
The second thing that we are going to talk about today would be the types of data, because once again,
this is a huge part of our data part.
So types of data.
There are two principal categories of types of data.
We have the qualitative qualitative one side or the categorical.
Basically, it's the same thing or and you also have the numerical or quantitative data.
So basically the first type of data, which is the qualitative data type, is not really there is like
no numbers and that you can't really measure what's inside of it with numbers.
And the numerical or quantitative type of data is really all about numbers.
Let's see what it looks like.
So the first step, as I said, would be the categorical one.
And inside of the categorical one, we have the nominal end of the data.
So for the nominal data, basically, it's pretty simple.
It can be used to label variables without providing quantitative values.
So once again, let's say, for example, you receive a questionnaire where they ask you what you prefer
to write down with your pencil, your pen, I don't know, a Sharpie, for example, or any other answer.
Once again, you can't quantify the answer.
You can only qualify this answer.
For example, the person answers with a pen.
So, OK, the answer would be a pen.
If we say the number of pens, the number of persons that prefer writing with pens, this would no longer
be a normal data.
The nominal data would only be the pen.
The number of persons that prefer writing with pens would be a it would be another type of data in this
case.
So you'd think it could be, for example, another example that we have here.
Let's say, for example, your favorite animal.
This could be once again another type of nominal data.
Why?
Because, well, in this case, the animals, you can quantify them.
And once again, there is no numerical value to it.
And another really important thing to understand, the difference between nominal and ordinal data is
that the nominal data is not ordered, but the ordinal data is ordered.
So let me explain it differently.
So, for example, when we if we take our first example, which would be let's say, do we prefer writing
with a pen or a pencil?
If I choose pen or if I choose pencil, there is no order between it could be a penny, could be a pencil.
It could be a Sharpie.
All the answers are the same, but ordinal data would be a bit different.
For example, let's have an example of ordinal data.
Let's say, for example, I'm asking someone, do you like this type of exams on a scale of, let's
say, for example, bad to excellent.
So if the person doesn't like it, well, the person will say bad.
If the person likes it, the person will say X.
And as you can see, there is a certain order between those.
Well, those emojis here, we have emojis, but it could be, for example, things like don't like,
OK, fantastic.
There is like a certain order.
But if we ask someone the difference, if they prefer a pen or a pencil or if they prefer a dog or a
cat, there is not necessarily a difference between those.
Well, there is not no difference between one or the other variable, so we can't really see the difference.
For us, it's pretty much the same thing.
So this is for our category parts of data.
As you can see, the main characteristics of those theories is that they can be quantified and they
well, there are really about qualitative, about about quality.
So it's really about the qualifying something, not really quantifying something.
All right.
Let's jump to the second part, which would be numerical and quantitative data.
So basically, in those types of data, we have two types of data which are which looks the same but
are very, very well.
They're not really different.
But while they're pretty much the same thing with some subtle differences so far, the discrete types
of data, basically those would be complete numbers.
So let's say, for example, we'll it won't be an infinity of possible numbers.
For example, let's take the age, the age of someone could not be, for example, one point five five
three four nine, for example.
It will be one years old, two years, three years old.
The height of someone will be, for example, one meter, 10 centimeters, one meter 80 centimeters
or six feet were five feet five, for example.
It's really it's something that is not infinite.
For example, once again.
For example, right here, we can have a sample versus consumer once again here.
It's something that is the has and if we're talking about continues the U.S. So once again, just to
fully to understand, this would be a point on the graph.
So there is nothing for example, let's let's talk about the cost versus the number of tickets.
If I buy one, think it's going to cost me, for example, 20 dollars.
If I buy two tickets, it's going to cost me in this case.
Forty dollars.
But I can't buy in this case, one point three tickets, which in continues would work because, for
example, in continues, you will have another thing which would be in this case, pound versus cost.
And in this case, you will have, let's say, for example, there is an infinite possibility.
So basically the difference between discrete and continuous is that in discrete, in the discrete type
of data, you don't have an infinity of possibilities, because right here, for example, we can have
the age versus the height and we have points.
And those points are infinite.
So there is and then, for example, we have each number one, each number to each, number three,
each number four.
In this case, it's different because we have an infinity of ages.
Even if we have complete numbers, we have an infinity of ages.
We have, for example, one point three, four or five years.
One point if you guys get the point.
So there is an infinity right here.
So this is why we can see that there is well, the line right here, that's a straight line in here.
We have point.
So that's that's the main difference between discrete and continuous type of data.
So we have the discrete type of data.
As I said, it's really infinite and continuous type of data that is that could be considered infinite
in the way that, for example, after the comma.
So it could be, for example, the age of someone would be 30 years, comma, five, four, three,
two, one year.
Once again, that's just an example.
So this is for what is the what is data, the data part.
So you understand what exactly is data, how it works, the different types of data.
So in this case, we talked about the numerical quantitative data as well as categorical types of data.
So I hope you guys understand all those concepts.
As you can see, it's pretty simple until now, if there is anything that you guys don't understand,
don't hesitate to simply watch this glass, because once again, this is pretty simple.
It's only the basics of what exactly is data.
So that's a first class guys and see all in our next class.