Hello and welcome back to another class of our course about the complete introduction to science, as
well as that analysis with the use of Python in this class, we are going to talk about the really important
part of the science, which would be statistics or basically statistics are not the base of data science,
but are a huge player in data science.
Indeed, a lot of techniques, a lot of statistical measures are used in data science, and this is
why it's really important.
So in this class, we'll just have a small introduction to statistics.
We're not going to talk about all the statistics, terminologies and all the statistics measures that
are used, but just have an introduction to statistics.
What is it exactly where it can be used in the future classes?
We are going to talk about the basics that you guys would need to have in statistics to be able to understand
that science.
So let's start.
So basically what the statistics say, if I go with a basic explanation of what is statistics, that
simply a branch of maths that will deal with the collection, analysis, interpretation and the representation
of masses of numerical data.
So basically, you take a population and you will work with, let's say, a sample of this population.
Indeed, it's possible to work with the whole population.
But once again, this could be really complicated because it's not always possible to, let's say,
for example, make service to the whole population.
So we will work with samples of population.
So let's say, for example, we want to work with, I don't know, with all the American population
to know what is their favorite color, for example.
So instead of asking all the papillae, all the American population will take different sample.
So we're not going to talk about sampling in this class, but this is just an example.
So you can take a sample, for example, from each city in each state.
So this is just one example.
And from that sample, since it's random people, you guys will be able we will be able to bring to
come to a conclusion that the favorite color of this sample is red, to say that the favorite color
of American based on a certain sample is red.
So this is, for example, this is one example of how statistics could be used.
And as you can see, this is some really basic data science things that we have here, statistics to
be used in many fields.
So as you can see here, it could be used in education.
Know, for example, I don't know which well, we can use statistics to see, for example, which in
which type of classes students perform the most or perform the less and try to find out the problems
in, for example, those classes.
For example, let's say students have problems in maths in this field.
Exactly.
Then we will try to find out the problem, why students have problems in this field.
Maybe it's because of the way it's presented.
Maybe it's because, I don't know, students are just not motivated by this field.
Maybe there is any.
Well, we'll try to find the explanation to this.
I mean, it's possible to do it with statistics as well as data science.
But once again, statistics will be the basics of all this could be used in stock market life science,
whether retail insurances and many, many other fields.
Once again, it's not used alone.
It's used with other mathematical fields as well as data science.
So right now, we'll talk about the types of statistics.
Basically, we have two types of statistics, descriptive statistics and inferential statistics.
We are not going to come into details of each of them.
But I'll try to give you a brief definition of what are those two types of statistics and just give
you a small example of what it could be.
So basically, for the descriptive statistic, it will help us organize the data and focus on the character
of data providing parameters.
So this would be for the descriptive statistic, and it's really used to well, it's the use of data
to provide a description of people of a population sort either through numerical or graphs or tables.
So this is basically for descriptive statistics and inferential statistics.
It's a bit different.
It's simply generalized generalized data set and applying probabilities to arrive to a conclusion.
And the inferential statistics will make inferences and predictions about population based on a same
sample of data taken from a certain population.
So basically, let's say that we have wine.
So let's say you guys are producing wine in huge quantities, quantities, and you guys want to know
what you guys want to look at the age of your wine.
So basically with the descriptive statistics that when you guys will start the age of your wine, you
will have the average.
So you will have the average age of your wine.
You will have the max.
So the max, the max, the highest age of your wine, the min, so the lowest and the wine that is the
less aged.
And basically divide it into three categories like this, inferential statistics.
It's going to be a bit well, it's going to be a bit different.
You will still have your average in the center, but you will have a category that will be, in this
case, aged wines, not max aged wine.
So you will just have aged wine where you will have all the winds that are aged more than a certain,
let's say, age, and you will have less aged wines.
So in this case, you will have, let's say.
All the ones that are less age than a certain age in this case will work more with the quartiles instead
of just working with Maxims and Minimum's.
You can also apply this to, say, population.
So a population that is short or small while a population.
So you want to know the height of a certain population.
So in the descriptive statistics, you will have the highest person, you will have the max height,
so you will have the highest person in the room and the shortest person in the room and you will have
the average.
So basically you have those three persons and inferential statistics.
You will have a category that will be tall, a category that would be short, and you have the average
as well.
So basically, those are for the two types of statistics.
I'm then why statistics are useful in data science.
So basically there are really useful for plenty of reasons.
But for me personally, I think that the two main reasons why they are really useful is that it will
give you a basic understanding of different ablazing methods and techniques as well.
So basically, data well, statistics brings to data science many, many techniques and methods that
could be used in data science to be able to, well, analyze.
We better the data.
So basically, this is why your statistics are really, really important data science.
And as I said, statistics are really the base of data science.
And this is well, this is why it's really important.
And the second reason is because it allows data scientists to use many measures such as?
Well, those are just some of them.
So we'll have the merrian the mean, the standard deviation and, well, many more to be able to make
a complete analysis of something.
Well, when I see something good, it could be, for example, in the financial field.
So let's say you want to analyze a stock, you can use the standard deviation.
And this is something that is really, really important because, well, for example, for a stock,
a high standard deviation means that the stock is very volatile.
It's a low standard deviation means that the stock will not move that much.
And once again, this is one this is something that comes from statistics and that can be used in data
science.
Once again, the median, for example, I want to find out the median of something I want to find on
the mean or the average of something.
Those are all good.
That's all coming from statistics and is very, very used into data science.
So what you see here are just some graphs of some analysis that have been made a while.
You will be able to perform those types of analysis.
Those are simply results of of analysis that have been made.
I don't know what those analysis, but those are just the results of the results.
Look, something like this.
And at the end of the day, this is a mix of statistics and the science put it together to be able to
analyze a population or a certain dataset, to be able to answer a certain problematic in this world
and to come to a conclusion about, well, about something.
And to solve a problem, so because this is the main goal of their science, as well as the statistics
in their science to solve a certain problem.
So you guys right now understand why statistics are that important.
Now it's time for us to start learning all the basics that will be really important for data science,
while all the statistical bases that will be really important in data science, for discourse and for
your future, as it are saying this.
So that's it for.
Guys, guys.
And see all in our next class where we are going to learn all those basics.