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Hello, guys, and welcome back to another class, of course, about data analysis and science, the

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complete introduction with the use of.

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So in this class, we are going to have a complete introduction to the concept of data to understand

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what exactly is data, how it works.

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And that is where you guys will have a complete knowledge about this subject.

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So let's jump right into it.

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So basically, what is data exactly?

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So there is the concept of there can be something really, general, your cell phone, for example,

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when you click on your cell phone.

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This could be considered as data when, for example, you are using, I don't know, your chips where?

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Well, there is data there when you were typing something or on your keyboard, it could be there as

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well.

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Basically, what will help you can see data could be seen as statistical sets or facts that can be used

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for analysis.

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So basically, let's say, for example, you guys are walking outside and you are making steps.

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Those steps could be construed as data because they could be used to know exactly how many miles you

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guys are walking each and every day.

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Let's let's say, for example, we want to calculate how many steps we need to do per day to be able

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to stay healthy.

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Well, the steps that you guys do each and every day can be counted to be able to say that, for example,

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you guys have walked 1000 steps and you guys are healthy.

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Well, we can make an average of everybody who is walking and say that, OK, in general, on average,

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each person that walks one thousand steps and there is a person that is healthy, basically not only

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steps, but as I said, it could be absolutely anything that can be quantified, but not only quantified

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because there are different stereotypes that exist.

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We are going to talk about it a bit later.

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So principally, we can have numbers.

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As you can see here, we can have quantities, informations, measurements, facts, observations and

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graphs as well.

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But graphs, I see them more as a visualization, too, because they allow us to see the big picture.

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So let's say, for example, we're talking about those steps as well as how many steps are making.

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So you will be able to visualize how many steps you guys are making per day with the graphs.

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Another thing that is very important to understand with data is that it have to fit for really well,

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four big things.

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First of all, it has to be able to be collected and stored so easily.

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All types of data need to be collected and stored so it can be collected and stored.

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Well, it's not really the same thing and has to be measured.

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So basically the data that you collected, you can be able to measure it so you can be able to measure

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it numerically or with any type of measurement.

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You should be able to analyze this data once again.

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You should be able if we're talking once again about our steps, this can be considered as raw data

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data.

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So this is really raw data.

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And those steps we can analyze them to, let's say, for example, know how many steps someone who is

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healthy is walking, but we can analyze it once again.

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And instead of having this conclusion, we can calculate, for example, on average how many steps each

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person makes.

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Then on average, how many persons per day are working at least 30 minutes a day, for example, once

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again.

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And we can do a lot of analysis with all this.

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And finally, your data should be able to be visualized.

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So once again, those steps, you can visualize them on a graph or on any other tool that can be used

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in data analysis to visualize things, to visualize what, let's say your conclusions.

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So in this case, we can visualize our steps on graph.

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So let's say one, the person walked this amount of steps.

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They do this person walk this and on the steps, et cetera.

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So this is for data.

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The second thing that we are going to talk about today would be the types of data, because once again,

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this is a huge part of our data part.

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So types of data.

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There are two principal categories of types of data.

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We have the qualitative qualitative one side or the categorical.

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Basically, it's the same thing or and you also have the numerical or quantitative data.

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So basically the first type of data, which is the qualitative data type, is not really there is like

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no numbers and that you can't really measure what's inside of it with numbers.

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And the numerical or quantitative type of data is really all about numbers.

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Let's see what it looks like.

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So the first step, as I said, would be the categorical one.

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And inside of the categorical one, we have the nominal end of the data.

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So for the nominal data, basically, it's pretty simple.

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It can be used to label variables without providing quantitative values.

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So once again, let's say, for example, you receive a questionnaire where they ask you what you prefer

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to write down with your pencil, your pen, I don't know, a Sharpie, for example, or any other answer.

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Once again, you can't quantify the answer.

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You can only qualify this answer.

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For example, the person answers with a pen.

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So, OK, the answer would be a pen.

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If we say the number of pens, the number of persons that prefer writing with pens, this would no longer

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be a normal data.

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The nominal data would only be the pen.

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The number of persons that prefer writing with pens would be a it would be another type of data in this

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case.

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So you'd think it could be, for example, another example that we have here.

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Let's say, for example, your favorite animal.

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This could be once again another type of nominal data.

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Why?

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Because, well, in this case, the animals, you can quantify them.

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And once again, there is no numerical value to it.

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And another really important thing to understand, the difference between nominal and ordinal data is

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that the nominal data is not ordered, but the ordinal data is ordered.

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So let me explain it differently.

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So, for example, when we if we take our first example, which would be let's say, do we prefer writing

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with a pen or a pencil?

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If I choose pen or if I choose pencil, there is no order between it could be a penny, could be a pencil.

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It could be a Sharpie.

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All the answers are the same, but ordinal data would be a bit different.

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For example, let's have an example of ordinal data.

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Let's say, for example, I'm asking someone, do you like this type of exams on a scale of, let's

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say, for example, bad to excellent.

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So if the person doesn't like it, well, the person will say bad.

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If the person likes it, the person will say X.

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And as you can see, there is a certain order between those.

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Well, those emojis here, we have emojis, but it could be, for example, things like don't like,

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OK, fantastic.

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There is like a certain order.

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But if we ask someone the difference, if they prefer a pen or a pencil or if they prefer a dog or a

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cat, there is not necessarily a difference between those.

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Well, there is not no difference between one or the other variable, so we can't really see the difference.

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For us, it's pretty much the same thing.

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So this is for our category parts of data.

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As you can see, the main characteristics of those theories is that they can be quantified and they

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well, there are really about qualitative, about about quality.

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So it's really about the qualifying something, not really quantifying something.

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All right.

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Let's jump to the second part, which would be numerical and quantitative data.

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So basically, in those types of data, we have two types of data which are which looks the same but

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are very, very well.

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They're not really different.

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But while they're pretty much the same thing with some subtle differences so far, the discrete types

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of data, basically those would be complete numbers.

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So let's say, for example, we'll it won't be an infinity of possible numbers.

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For example, let's take the age, the age of someone could not be, for example, one point five five

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three four nine, for example.

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It will be one years old, two years, three years old.

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The height of someone will be, for example, one meter, 10 centimeters, one meter 80 centimeters

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or six feet were five feet five, for example.

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It's really it's something that is not infinite.

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For example, once again.

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For example, right here, we can have a sample versus consumer once again here.

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It's something that is the has and if we're talking about continues the U.S. So once again, just to

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fully to understand, this would be a point on the graph.

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So there is nothing for example, let's let's talk about the cost versus the number of tickets.

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If I buy one, think it's going to cost me, for example, 20 dollars.

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If I buy two tickets, it's going to cost me in this case.

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Forty dollars.

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But I can't buy in this case, one point three tickets, which in continues would work because, for

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example, in continues, you will have another thing which would be in this case, pound versus cost.

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And in this case, you will have, let's say, for example, there is an infinite possibility.

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So basically the difference between discrete and continuous is that in discrete, in the discrete type

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of data, you don't have an infinity of possibilities, because right here, for example, we can have

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the age versus the height and we have points.

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And those points are infinite.

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So there is and then, for example, we have each number one, each number to each, number three,

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each number four.

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In this case, it's different because we have an infinity of ages.

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Even if we have complete numbers, we have an infinity of ages.

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We have, for example, one point three, four or five years.

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One point if you guys get the point.

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So there is an infinity right here.

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So this is why we can see that there is well, the line right here, that's a straight line in here.

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We have point.

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So that's that's the main difference between discrete and continuous type of data.

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So we have the discrete type of data.

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As I said, it's really infinite and continuous type of data that is that could be considered infinite

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in the way that, for example, after the comma.

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So it could be, for example, the age of someone would be 30 years, comma, five, four, three,

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two, one year.

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Once again, that's just an example.

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So this is for what is the what is data, the data part.

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So you understand what exactly is data, how it works, the different types of data.

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So in this case, we talked about the numerical quantitative data as well as categorical types of data.

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So I hope you guys understand all those concepts.

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As you can see, it's pretty simple until now, if there is anything that you guys don't understand,

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don't hesitate to simply watch this glass, because once again, this is pretty simple.

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It's only the basics of what exactly is data.

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So that's a first class guys and see all in our next class.