Oh, yes, and welcome back to another class of our course about the complete introduction to that science
with Python.
So basically in this course, we are going to talk about the basics of data science.
So this part of the course is really theoretical.
We are going to talk about the theory behind that science and everything that you guys need to know
about this subject.
So let's start.
All right.
So basically, what is data science?
So when we ask someone what is exact data science or how it works or anything else?
Well, it looks very fancy in the word.
Data science looks really, really fancy.
But basically, data science is simply the study of data.
Yes, you have a lot of positions that can exist in data science to talk about them a bit later.
But basically, data science is really the study of data so that science can start as early as the recording
part.
So let's say, for example, you have a project and you want to find out a solution to a certain project.
So the first thing that you want to do is recording the necessary data to be able to answer the problematic
inside of your project.
So basically, let's say, I don't know, you want to know how many will you give a survey to some people
and those people fill up the surveys.
So let's say there are people who fill up the survey and I don't know, 100 percent say they like the
color blue.
And let's say of those 100 persons that like the color blue.
I don't know, 80 percent said that they like hamburgers as well.
So you can link up hamburgers with the color blue and say in majority of cases, people who like the
color blue, like hamburgers.
So you can, for example, create well, this could help you for marketing for a lot of purposes.
But this is basically data science.
Like this is a really basic example of what data science can do.
So the study of that, I can start as early as in the recording phase.
Recording phase could be, for example, giving people well, giving people questionnaires where they,
for example, well, service, I mean, surveys, stories that people can take.
So, for example, do like the color blue or the like the color red.
And once again, those surveys are linked up with your problematics.
So very important.
You at first need to have it problematic that you want to solve.
And for this problematic, you need a certain certain data from certain people and you will give them,
for example, surveys.
It could be surveys, it could be just ask them questions.
There are plenty of ways that exist to be able to record data.
The next step would be storing the data, because once you have the data, you need to store it somewhere.
You don't know if you have the data physically, if, for example, it's on papers and you need to store
this data.
So you need really a storage room if it's going to be stored on the hard drive of your computer, if
it's really huge amounts of data, maybe it's going to be stored somewhere on the server, maybe it's
going to be stored on the cloud.
So data science is really all those elements together.
Next thing would be analyzing this data.
So once again, transport, analyzing all the and the like sports or you want to take the raw data and
you want to transform it into data that you can use to be able to fix your problem.
Problematic, sorry.
So basically this path is really, really important.
And the majority of times people not only analyzing it, but they don't know the storing and recording
part.
But all this is part of data science and of the scale of the data data transformation.
So the second part right here is just, well, another part of data science.
So basically, data transformation could be part of the analyzing part right here.
Basically, data transformation is part of data science.
And in this case, this could be extracting the right information.
So when we're talking about data science, as I said, we work with huge amounts of data so we can have
millions, if not billions, sometimes data of peoples, depending of what type of problematic you want.
If you want to fix, for example, we talking about marketing were a specialized, specialized ads.
For example, when Google once when Google is able to give you like perfect ads for you, it will collect
data on you.
And with those patterns and with all the data that they collected, they took this raw data and they
transformed it.
And after that, they are able to know exactly what type of advertising they will give you.
So really extracting the right information to be able to well after that, transform it, gain insight
into useful clues to run efficiently.
A project once again.
Right now I'm talking about Google, but it could be anything else.
It could be, for example, in the manufacturing field, for example, I don't know if you call it on
the machine, for example, let's say the data is collected on a on real time.
Let's say, for example, data is collected on real time.
Let's say we spot a.
Problematic each time that I don't know, there is I don't know, there is a certain peace that is created
or there is one hundred pieces that are created on this machine and there is a problematic each one
hundred pieces.
Well, with data science, we are able to spot this problematic and say, OK, each time that this machine
creates one hundred pieces of something, we need to make maintenance on this machine.
If we don't want it to break, then that well, data science can also be used to predict certain outcomes.
So once again, data science can be used in the planning of fields and predicting outcomes could be
well, for example, in the banking world, in the financial world, it could be as well in the manufacturing
world.
In marketing, as I talked a bit earlier right now, this could be part of it.
So, for example, giving specialized ads or ads that are perfect for you, this could work as well.
So this is for what is that word?
Data science is used.
Data science is used in many, many, many fields.
Those are just some examples.
So, for example, for online businesses or e-commerce, which is really popular right now, we can
say that a science could be used to recommend products.
So product recommendation, better marketing as well.
If, for example, you need the well, let's say a certain business wants to market the right products
to the right audience, well, it will help them.
So, as I said, if, for example, you go on Amazon right now and you start Googling certain products,
you write down a certain product on Amazon.
Well, Amazon will take this data and will give you products that are recommended based on your best
research.
So this is all data science, health care, for example.
You can use that as science and health care in a lot of ways to, for example, virtual assistant.
Let's say, for example, there are systems that are virtual, not right now.
And this could be an amazing way to well, to use that as science, it could improve medical researches.
Since we have a lot of data, we have a lot of knowledge about a certain subject.
For example, if we see that, well, I don't know this medication, how well this medication tested,
I don't know, 1000 people help them with this in this in this symptom, for example, this could be,
well, pretty much a good thing for image analysis or in this case, picture or picture analysis.
This could be a really good thing as well in the health care in the health care world, sorry, in the
banking industry, for example, for fraud detection.
Well, it's not an employee that is sitting in his office and looks for frauds.
It's complex algorithms that are created and that works for fraud detection.
And it's all about data science and machine learning, all the stuff to create credit scores, for example,
and to give the right product to the consumers.
Yeah, there is a little mistake right here.
So giving the right product to the right consumer, especially in the banking world, sometimes it's
well, it's not really easy because customers, some customers have millions of dollars or other customers
don't have money at all.
So it's really well, it's completely it's it's really different.
And you can't offer a type of product that you offer to the one to the other one for transport, for
example.
Yes.
Optimization in this case, it's going to be let's say, for example, you want to go for place from
place to place B, how to go the fastest way possible.
And they're really Real-Time optimization.
So in this case, it's all about data science.
For example, it will know that there is traffic at this place and it will not suggest you to pass by
this place or this place.
Self-driving cars could also be an example of how data science can be used, because once again, this
is a huge amount of data that is collected to make the car driving alone personalised virtual assistant.
Once again, it's like the health care part for manufacturing.
As I said before, we have problem detection.
That could be really an amazing way and the problem of prevention as well as automation.
So let's say, for example, you want to automate your production.
It's possible with data science.
So this so for example, with all the data that is collected, you are able to hire engineers as well
that are engineers will be able to create machines that will automatically work and will automatically,
let's say, for example, well, work on huge skills.
So let's just take a basic example.
Let's say a huge business that is, I don't know, putting water inside of bottles.
So it's all automated.
There is no one person that is like putting water in each bottle.
It's really a huge machine that is automated because of the use of data science.
Once again in finances.
It's really, really used in algorithms, algo trading, algorithms, algorithm trading and that.
This is all algorithms that are created based on past performances of a certain stock or ETF or whatever,
and it's based on all this data that algo traders are able to create programs that will trade by themselves
and really control the race, creed, all the strategies around the around their trading style, which
is pretty much a very cool thing.
So those are some users of data science, but it can also be used in engineering.
It can also be used in agriculture.
It's used pretty much everywhere.
And it's really part of our lives right now.
All right.
So next thing is about the data scientists.
So basically, what are the skills that you will need to have?
So for me, once again, this is based on my personal experience.
You need the three skills.
Even if you don't necessarily have them now, you can work on them.
So basically logical thinking and mathematical understanding so that science is all about mathematics.
So there is a lot of numbers.
There is a lot of well, you work always with numbers.
So if you're bad in mathematics, well, I'm sorry to say that, but it's going to be a bit hard for
you.
So really having a good mathematical understanding and logical thinking because mathematical understanding
is linked with logical thinking.
So you need to understand basic logical concepts.
And with those basic concepts through that, you can put them on the higher and higher and higher skills
and create something that is pretty cool.
But just understanding the basics sometimes.
Well, the moment you understand the basics, you will be able to go well further and further and further
understanding the business world around us, because there are science is not just a thing that you
play with, that science is really something that you will use to solve real problem issues.
Well, an example.
Let's say, for example, you want well, you the first thing the first step in data science is really
to find out the problematic that you can solve.
You will not just collect data to collect data because this don't make sense at all.
This is this would be raw data that will not be used and doesn't make sense.
Just collecting data for collecting data if you collect data is to improve something is to reach a certain
goal or a certain objective.
This is why you need an understanding of the business that is around this world, the business world
that is around us, because in the majority of times the use of data science would be done inside of
business.
World's interest in technology and programming, data science nowadays.
Well, mathematics is the first word, but programming and technologies are the second part.
As I said, that science is not only in the analysing part where you analyze the numbers, it's well,
it starts from the moment that you guys will give surveys, for example, and you collect data and store
data.
So you need to understand all the technological parts around this.
What what are the things that you can use, for example, to store data, how you can collect your data,
and so that all the techniques that you can use to analyze properly your data?
Yes.
The more you get well, the more you work with that data science and the more you analyze data, the
more you will get you will create your own techniques and you will have your own ways to analyze data.
And you will see it's not something that is, well, that complicated at the end.
It's just logical thinking.
Well, it's 100 percent logical thinking on this thing that assigns jobs.
Well, you can have a lot of data science jobs that are not listed here are just listed.
Three examples.
For example, that analysis database administrator or business analysis, those are the jobs that come
to me like quickly right this right now.
But you could have, for example, data engineer, you can have data scientist, you can have business
analysts, you can have statistics that this statistician, statistician, statistician.
So there are plenty of other jobs that are linked with the data science, which is pretty cool.
So you'll see this field is already pretty cool.
So the basics of data.
So right now we are going to talk a bit more in depth about the basics of data and the data lifecycle.
And basically, in this case, there is six steps that we need to follow for data.
So just for you guys to understand how exactly it works.
So it's pretty simple.
The first step would be, let's say, for example, you are in the business in the first step will be
identifying the problematic that you want to solve.
So let's say, for example, I don't know you.
Let's see, just a really basic example.
Let's take our example of Hamburger's you want to create a new packaging for your hamburger.
So let's say, for example, you working for, I don't know, a hamburger store and you have thousands
and thousands of consumers and you give them I service, let's say it's online surveys and they fill
fill up their servers.
So you have your program at your first step will be identifying.
Your problematic.
So your problem is I want to create another packaging for my hamburgers, then the next step would be
finding a way to collect or acquired data so you can collect yourself through that or you can ask a
business to acquire data for you.
Yes, you need to pay a certain fee.
But once again, if you are a big corporation, you don't really care about paying those fees because
at the end of the day, you will make profit because of all this research around it.
So the second step will be finding a way to collect this data.
So the first step well, the first thing that you need to understand here is what data you need.
So basically, you need to understand the data that you guys will need to collect what it because if,
for example, you want to change your packaging, you will not ask your consumers, do you like it when
it rains outside?
It doesn't make sense.
So, for example, you will ask them, hey, do you prefer blue or red, for example?
So they will answer the question.
Next step would be thinking about where I can find this data.
So once again, you can acquire it.
You can buy it from, let's say, another business.
Already did this research.
You can buy it from the business.
Well, you can buy it from the company that did the research for that business.
Or you can go and collect the data by yourself.
And then the most efficient way to obtain and store this data, because once again, this is part of
the first step.
So you'll think about a good way to tell an efficient way to collect is that if you want to collect
it by yourself, you will think about an efficient way to collect that.
I could be, for example, giving surveys to your consumers where they come when they come to your restaurant,
or it could be sending them emails where you tell them, hey, you get a free hamburger if you, for
example, fill up this survey.
All right, that would be data transformation.
And in this case, you want to put your data in to the right format and you will clean your data.
So let's say, for example, you have surveys that are not filled up to the maximum.
You want to clean this data, all the missing values, you want to take them away.
You want to come compute all the data.
So put everything together of the corrupted data, all the applications you want to take them away.
So let's say, for example, someone to fill that up, the survey like five times because he wanted
50 hamburgers.
Well, you only counted as one person and removing unnecessary data.
So let's say someone just wrote, I don't know anything inside of history.
Well, you just take him away as well.
Next step is when you have well, when you have all your data ready this that you have all your raw
material.
So in this case, you have all your raw data.
Now it's time to understand it and transform it and create the solution to your problem.
So once you have all the raw data, you want to understand it.
So you will make hypotheses.
So say, for example, you will say, hey, that's a thing that we talked about before.
So, hey, one hundred percent said they love the color blue and 80 percent said they like hamburgers.
So basically, if it's if it's this way, this means that everybody eats a hamburger or the majority
of people who are eating a hamburger likes the color blue.
So basically, maybe this could be a good thing to have a blue packaging for my hamburger.
So this would be making a hypothesis and at the same time understanding the data pattern.
So we get our data.
So we have our data and we understand that this brings us to this.
And this is where you really need logical thinking.
Once again, this is a really basic problem.
Sometimes they're way more complicated.
So this would be for the fourth step, really understanding the data.
So hypothesis you will make them there.
And once again, understanding of the patterns around the data that you have a step five will be modeling
your data.
So once again, you will create a model around this data.
So in this basic example, it will be, for example, creating packages.
So let's say you create a blue package.
In this case, you want to evaluate the efficiency of your model.
So let's say, for example, you create those packages and you give them to your consumers.
And if they're happy, well, this would be a good way.
So once again, you will deploy.
But once again, since our example is really simple, let's say we keep we'll with this example, you
will deploy, but only small scale.
So let's say, for example, you will deploy one day a week.
So you will have your new packaging one day a week and you will give them the give it to your new consumers.
But they will be able to see it.
They will be able to use it.
And who knows, maybe it works perfectly.
So when all this works perfectly, so you're in your step five.
If you see that your consumers are happy, your sales are going up because you change your clothes,
you change the packaging of burgers and they're blue now and everybody is happy.
It's time to go to the store.
Six, which is launching the solution or launching your model on a larger scale.
So let's say you have a few restaurants, so instead of launching your model on only one restaurant,
you will launch it on more than one restaurant or inside of all your restaurants.
The best way to do this is really going step by step.
So when you reach the step five, you launched your model or your solution inside of a small scale.
And the next thing would be inside of it larger scale.
So you always want to limit your risk.
So never lunch at a whole scale straight up, because what can happen is that you could have world problems
where anything could happen and your solution will just break.
So you lunch on a small scale and you slowly grew your lunch and everything if everything goes the right
way.
This means that you can just launch it faster.
So those are the six steps inside of that as well.
The basics of data.
Those are the six steps that we need to follow.
So really, from finding the problematic to launching the solution, once again, those are a really
basic example.
So this is really for the basics of that right now.
We will jump inside of the course.
We are going to start talking about the tools that are used inside of the restaurants.
And you'll see basically this course will teach you all the basics about the subject and you will have
a lot more knowledge when this course will be done so that first class guys in C all in our next class.