Original subtitles

Instructor: You are probably watching this course

because you wanna learn the appropriate statistics

to perform different tests.

Maybe you wanna use this knowledge as a stepping stone

to a career in data science.

Either way, before we can start testing

we have to get acquainted with the types of variables

we usually encounter.

Different types of variables require different types

of statistical and visualization approaches.

Therefore, to be able to classify

the data you are working with is key.

We can classify data in two main ways

based on its type and on its measurement level.

Let's start from the types of data we can have.

There is categorical and numerical data.

Categorical data describes categories or groups.

One example is car brands like Mercedes, BMW and Audi.

They show different categories.

Another instance is answers to yes and no questions.

If I ask questions like, Are you currently enrolled

in a university or do you own a car?

Yes and no would be the two groups of answers

that can be obtained.

This is categorical data.

Numerical data, on the other hand,

as its name suggests, represents numbers.

It is further divided into two subsets.

Discreet and continuous.

Discreet data can usually be counted in a finite matter.

A good example would be the number

of children that you want to have.

Even if you don't know exactly how many,

you are absolutely sure that the value will be an integer

such as zero, one, two, or even 10.

Another instance is grades on the SAT exam.

You may get 1,000, 1,560, 1,570 or 2,400.

What is important for a variable to be defined as discrete

is that you can imagine each member of the data set.

Knowing that SAT scores range from 600 to 2,410 points

separate all possible scores that can be obtained is key.

It's easier to understand discrete data

by saying it's the opposite of continuous data.

Continuous data is infinite and impossible to count.

For instance, your weight can take on

every value in some range.

Let's dig a bit deeper into this.

You get on the scale and the screen shows 150 pounds

or 68.0389 kilograms, but this is just an approximation.

If you gain 0.01 pound, the figure on the scale

is unlikely to change but your new weight

will be 150.01 pounds or 68.0434 kilograms.

Now, think about sweating.

Every drop of sweat reduces your weight

by the weight of that drop, but once again,

a scale is unlikely to capture that change.

Your exact weight is a continuous variable.

It can take on an infinite amount of values

no matter how many digits there are after the dot.

To sum up, your weight can vary

by incomprehensibly small amounts and is continuous

while the number of children you want to have

is directly understandable and is discreet.

Just to make sure, here are some other examples

of discreet and continuous data.

Grades at university are discreet, A, B, C, D, E, F

or zero to 100%.

The number of objects in general,

no matter if bottles, glasses, tables, or cars,

they can only take integer values.

Money can be considered both, but physical money

like bank notes and coins are definitely discreet.

You can't pay $1.243, you can only pay $1.24.

That's because the difference between two sums of money

can be 1 cent at most.

What else is continuous?

Apart from weight, other measurements are also continuous.

Examples are height, area, distance and time.

All of these can vary by infinitely smaller amounts,

incomprehensible for a human, time on a clock is discreet

but time in general isn't.

It can be anything like 72.123456 seconds.

We are constrained in measuring weight, height, area,

distance and time by our technology, but in general

they can take on any value.

All right, These were the types of data.

In our next lesson, we will explore

the levels of measurement.