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Data Science Training with Python – Everything about Data Science training with Python

It has not been many years since the invention of the computer and the emergence of related technologies, but during this time many revolutions have taken place. 

The people we see are so involved in technology and its devices that we have to take technology addiction seriously.

Every piece of software and website you see is made up of a set of codes, which are like bricks and materials that make up a house. Data plays an extremely important role in our world. From a school system to very large organizations, which somehow all need a lot of data.

Imagine a school that does not have a list of students! Or is it possible to manage a school attendance schedule without keeping track of each student’s data and all the other data needed?

Certainly not, we need data collection and storage everywhere. Sometimes we have to analyze them, sometimes we make them simpler, sometimes we delete them, sometimes we update them, and data science can be considered an emerging science that can save many companies and create many jobs. And even improve other sciences.

In this article, we want to discuss Data Science education with Python . First, it is necessary to know such things as data science, Python and other data science programming languages, data science training books, data science job positions, Examine the importance of data science and data science education with Python (Data Science).


Everything about Data Science training with Python

What is data science?

What is data science in the simplest possible way? It should be said that gaining knowledge and knowledge of a set of data means that you use the data you have to understand something or discover something.

In simpler language, it means obtaining information in order to use it (in many external and internal websites, the definition of data science is more complicated. This definition of data science is a very simple and understandable definition for everyone, but it is not a complete definition.

Read this article to know more about examples and other definitions of data science. In fact, data science is an interdisciplinary science. To gain knowledge and awareness of data and information that uses methods, algorithms, scientific systems and processes to gain knowledge and insight from structured and unstructured data.

Structured and unstructured data

In the case of structured and unstructured data, structured data is data that is comprehensible to a computer. This means that the computer can process the data quickly with this data. Like data in databases or, for example, Excel software data, but unstructured data is like data that is not in databases.

A clearer example is the data in videos, news, songs, and more. This data must be structured in order to be able to use it in data science, data mining and machine learning .

In other words, any type of data that a computer can quickly process using predefined algorithms is structured (this explanation was needed because you may repeatedly name Python during data science training with Python. See structured data and unstructured data)


Structured and unstructured data

Specialization in data science is one of the needs of our time

If you still do not understand the concept of science, we will explain this concept to you with another example.

Imagine you know statistics, math, programming, and data analytics. Think about how much this science can help you use the data you get (such as information about your users’ personalities). Improve your business.

For example, you can offer the best product that users really need only with the help of data science! We will go through more details and follow the data science training with Python ( if you still have trouble understanding data science, you can refer to the articles and videos available on the web and then continue the article ).

The importance of data science

Perhaps one of the most important benefits of data science for companies is decision-making power. Data science is extremely important in the main decisions of companies.

With information from user data, you can make the best decisions and strengthen your business. What do users want? What are they looking for? What do they like? What are they used to? What is the average age of most users of a product? Or even things like what colors are most involved in attracting the user? It can also make decisions very easy for service providers.

Company Netflix (an American company like films that online service film and serial) data used to the tastes of the users in movies and series and that in addition to using a variety of information, even this information plays an important role in the project Making a new movie.

They always want to know what movie most users like to make. Data science can be considered as a requirement of all companies large and small to promote business. But because it often takes a lot of time and money, especially when dealing with unstructured data.

Only a few companies invest in data science. (Consider also that hiring data science specialists costs a lot of money and these people want very high salaries because to specialize in data science you have to learn several sciences well, spend many years learning and certainly Achieving this job requires high intelligence).


The importance of data science

Python programming language

If you want to become a science expert, Python and R are the best options at the time of writing this article . (Most textbooks and instructional videos are from these two programming languages.) Because this article is about teaching Data Science with Python, we have nothing to do with R at the moment.

But you should be familiar with the Python language. Python is a general-purpose programming language (it can be used to build a variety of software, such as:

  • (Web applications or mobile applications)
  • Open source (anyone can develop this programming language and make a new version of it)
  • Object-oriented (real-world concepts should be used for programming)
  •  High level (these types of languages ​​are more like human languages ​​and easier to learn than low level languages)

Advantages of using Python programming language

This programming language is currently managed by the Python Software Foundation. One of the advantages of Python over other programming languages ​​in the world is that the readability of Python code is extremely high and you do not waste time on your teamwork.

Another advantage of Python is its simplicity of code. That is, if in other programming languages ​​a hundred lines of code are required to perform an operation, in Python the same will be done with twenty lines of code.

There are an infinite number of libraries available for Python, which is why Python is great, and that’s why Python can build software for machine learning and data science.

In fact, with Python you can use data science for various purposes. Programs that are part or all of the software written with Python:

  • Instagram
  • زوپ
  • یام
  • Milman
  • BitTorrent
  • Chandler
  • Plon and Campillo and…
important point

To get started with Python and Data Science, you need a programming environment where you can write your code, debug, and see the result of the code. One of the best programming environments for working with Python and data science is the Anaconda environment.

This environment can use this link  to download the. Due to the simplicity of how to install this environment, we avoid explaining about this, but if you have problems with the installation, refer to this link (


Advantages of using Python programming language

Data science specialist, jobs in this field

One of the most important things you need to know about learning Data Science with Python is the importance of data science and its job position. If you are planning to immigrate, especially to highly developed and ideal countries such as the United States, Canada, Australia or even European countries, then you definitely need to learn data science.

Because one of the jobs required by these developed countries is a data specialist or data scientist (generally jobs related to data mining, data science, machine learning are jobs that are much needed in the future).

In a country like Iran, we may not see many job titles for data engineers or data scientists right now, but do not forget that as long as you learn this science, many things will change and according to research, millions of new jobs in A not-too-distant future is emerging to replace old jobs.

Certainly many of these jobs will be related to data science and machine learning, and those who consider this job to be a professional have very high salaries (it is very likely that in the next decade the highest salaries will belong to scientists).

In Iran, Shahid Beheshti University was the first university to cover the field of data science, and we will definitely see more growth of this field in universities in the future. Data science is said to be the most fascinating job of the 21st century, but it’s not that hard to understand because all businesses really need real insight into their data.

Some reasons to learn data science

Note : It’s time for the main topic of the article, Data Science Teaching with Python In this article or any other article, you will not be able to learn or specialize in data science.

Because if you really want to learn this science, you need several years of effort (you have to take heavy courses and read several books), but in this article we have summarized the importance, training and familiarity with data science. ام.

First of all, it should be noted that nearly 67% of the world uses the Python programming language for data science, and this means that the highest percentage of using a programming language for data science. Data will grow by 15%, until next year (if it grows by 17% every year, then it will be one of the most sought after jobs in the world in the coming years).

The third case is that the average income of a data expert has been announced as 127918 dollars, which means something around 115 million tomans per month! Of course, do not expect such rights in Iran, but certainly in Iran, data specialists will have very high salaries.

In the following, we will go step by step to get acquainted with all the steps of learning data science with Python.


Some reasons to learn data science

 The first step in teaching Data Science with Python

The first step in learning data science is learning the basics of the Python language. This means that first you have to know programming and then you have to learn a certain level of Python programming language.

If your field of study is computer or you know the basics of programming, it should be said that you are somewhat ahead and Python is a very simple programming language and do not worry because your speed in this area will certainly be high. (You will quickly master Python).

If you know the Python programming language yourself, that would be much better, and you have to say that you are part of the way of becoming an expert. But if you do not know the basics of programming and no programming language is still a problem, there are many courses and articles on the web for you to learn that are nearly 80% free and because Python is more than any other language. Programming is easier so you can master it soon.

Of course, you do not need to learn Python professionally. Or, for example, to build mobile applications (you have to use Python libraries to do this), but you only need to know the basics of Python (the average level of Python is enough) below is an example of Python code for your familiarity:



In this example, first two variables are defined. Then both are added to another variable and in the last line of the result of the addition, two numbers and the corresponding text are shown on the screen.



The result of the eight lines of code above the screen

This was just a small example. To learn Python, you can take countless courses (one of the good Persian language courses is home school courses ). You can also read books (most books are in English) and be sure to participate in programming forums. There you will find answers to many questions and you can ask questions.




The book Python in simple language can be a good resource for Python newcomers. This book is by Mr. Younes Ebrahimi and is available in online bookstores. One of the features of this book is its completeness.

Learn statistics and probabilities

The next step is to learn statistics, probability, and math. Most likely you have an average knowledge of mathematics, even if you do not know anything about mathematics, try to engage in mathematics a little more on the Internet. As for statistics and probabilities, it should be said that statistics and probabilities are an important part of data science. If you did not pay much attention to statistics and probabilities in college or school, try to start from scratch (a lot of time to specialize in statistics And probabilities are not needed, of course, to learn data science).

In data science, you have to think about learning statistics more than being a programmer. You can probably guess why statistics is important in data science. You need to collect information and data in data science.

Also sort them and analyze all the possibilities for the progress of companies and businesses using structured and unstructured data.

Online bookstore

For example, in an online bookstore: What percentage of readers like science fiction books? What percentage of science fiction readers are over 45 years old? Which authors are more popular science fiction books? Why do people like such books? Statistics and Probability Science The company needs to collect and categorize these statistics, examine probabilities and provide all these types of information.

You can take many courses to learn statistics and probability, but it is better to learn statistics only for use in data science and not all the science of statistics and probability, so we introduce a good book called “Applied Statistics for Data Scientists: 50 Essential Concepts.”


Applied Statistics for Data Scientists: 50 Essential Concepts


This book talks about the importance of data analysis and its practical methods for solving different situations. You will also learn the basic techniques used to predict data and device programming.

So far, you are expected to work well with Python and collect, analyze, and analyze data using statistics.

Learn to work with databases

The next step in learning data science is working with databases. You will first learn to work with the Python language itself, then you will learn statistics and probability and mathematical knowledge so that you can collect data, analyze it and turn it into useful insights and knowledge.

The next step is for you to learn that once you have collected the data and examined some of the calculations and probabilities, you can convert this data into structured data and then store the structured data in the database. And learn to extract this information from the database whenever you want.


Simply put, the third skill you need to learn is working with a database like SQL (which is why data science is called an interdisciplinary science, meaning it is made up of many sciences and you have to learn them all). To connect to Python Fortunately, the Python programming language can communicate with all databases, and you can learn any one you want.

If you do not know what a database is, I must say that in the process of building a website or software sometimes you need to store a lot of data. And that you can quickly retrieve this data whenever you want.


Learn to work with databases


A database is software that enables you to store and retrieve your data quickly. Each database is built to communicate with one programming language (although some databases have the ability to communicate with multiple programming languages) and of course all existing databases. They have the ability to connect to Python, and this shows that Python is a wonderful language.

Here we need to introduce the SQL database. Because most courses use SQL. We are also reviewing this database here.


SQL is a high-level language used to create, modify, and retrieve data and operations on it. SQL is a declaration-based language, which means that unlike many programming languages, it is not suitable for problem solving (in other words, it is not possible to build software or design a website with this language. It is built to work with data. Some parts of any website or software can be built with this language (of course, its database part, not other parts).

By now you may have understood why it is said that it takes several years to become a science expert. Because you need a lot of science to get this job. Working with databases is another important part of data science. If you have already programmed and mastered one of the databases, you can connect to Python with the same database and store and sort the data in the database.

Of course, it is better to learn SQL to use countless courses on the Internet. SQL is not a difficult language and you need two months to master it

Sample SQL codes:



Show all columns of the book table provided they are priced at 10,000 and sorted by title. This was a sample translation of the above code. In SQL, select the columns from, select the table where it states the condition, and specify the order by type of order.



It connects the two tables of customer and books, and only the rows with the subscription are retrieved, and the type of order is based on the family of the customers.



In the above code you create a new data (also called an insert operation in the database). The above code says to add new columns one, two and three rows to the my_table table. You will work according to this order.



The above command is the delete command. This code says to delete the value N from the my_table column of field2 column. This command is also very likely to be used in data science when working with Python.

Python libraries to work with data science

If you are not a programmer or you are not very familiar with Python, we must tell you that one of the biggest advantages of Python is having a large number of libraries and frameworks.

This means that the Python language can not do much without these libraries, and owes all of Python’s versatility and ability to work with machine learning, data mining, and data science to these libraries.

In fact, all programming languages ​​need multiple libraries to demonstrate their power. This is where you need to start learning about Python data science libraries, but first you need to know the basics of Python. As we said in the first step and then here it is the turn of the libraries needed to work with data:


NumPy is a library of linear algebra in Python. This library is very important. The main use of the name Pi is for working with numbers and scientific purposes (in the field of working with numbers, it can be considered the best Python library).




The Pi name library is specifically designed to work with arrays and multidimensional matrices. The Pi name library is a very useful library for performing logical and mathematical operations on arrays.

Before you start programming with Anaconda, run this conda install numpy code on the command promet.

Build a one-dimensional array with 25 random integers:



Convert it to a 2D array with reshape () function:



Use Max () and Min (); We can get the minimum and maximum values ​​in an array:



The following is an example of linear algebra to learn more about how to work with matrices:



Transform a matrix



Display a 3-by-3 matrix of random numbers



Multiply the matrix



Get acquainted (gain, obtain) with present-day techniques that came from Python. Remember that programming means practice, practice, and practice again. Get started now and practice a few simple code examples.

Learn to use the keyboard and always test everything you learn yourself and sometimes change the code. Do not be afraid of the result, the programmer has to face hundreds of errors a day. However , we also recommend the for loop iteration article in Python .

Matt Plott

It is a library of Python, which is used to draw diagrams. When working with data science in Python, you definitely need to draw accurate and various diagrams. This library will start your work.

Sample Met Plot Library Code:



The result of this example is the code in the figure below. In this code, libraries named Pi (to work with numbers) and Metplot are used.


Libraries named Pi


A sample code from the previous example except that in this example all triangles are set by the user. As in the previous example, each triangle is made up of three points, except that here you specify the size of the triangles and their location:



In this example, the library named Pi is used along with the Met Plot. You can see the result of the above code in the figure below.


Name Library with Matt Plott


These two examples of code were just to make you more familiar with coding and teaching data science with Python. You can find and test countless more examples on the web.

The third most important library of data science is Pandas. Undoubtedly, Pandas is currently one of the main Python libraries for data preparation and processing. Pandas is open source, highly efficient, and provides data analysis tools for the Python programming language.

Pandas is one of those libraries that says it should be used for data science in Python. Plus a powerful library for preprocessing, visualizing, and analyzing data. Examples of Pandas code:



In the example above, we created a series of values ​​that show the default integer of each value. The output following the word Out is just two lines of code. Check it out carefully, the two Pandas libraries and the Pi name are used in this example.

Continuing the path in teaching Data Science with Python

So far, you have come a long way in becoming a science expert, but there are other important things to know.

Learning machine

Data science and learning machine are very closely related and can be said to help each other. If you want to learn data science, you will definitely need to know the learning machine. Because many times you will need to use a learning machine to analyze the data.

This means, for example, designing a site to categorize all relevant statistics and send them to you instantly. Learn more clearly the website that gives you information and statistics in all cases, such as visits to a specific hour of the site, to In general, machine learning means learning a machine like a computer that does some work.

Techniques related to unstructured data: You will definitely need to convert unstructured data to structured data for use in data science.


Continuing the path in teaching Data Science with Python

Having important and necessary information from the field of activity

If you want to be a scientist, this is your first step in starting a company. You need to know the company and its field of activity and especially your field of activity, so that you can use data science to solve problems and shortcomings and provide excellent solutions.

One of the benefits of data science was the facilitation of decision-making power. In order for the organization or company you work for to make good and smart decisions, you need to know your field of activity and use data science to make the best decisions for the future of the company and the improvement of products.

communication skill

It may not be wrong to say that a data scientist does not have high communication and verbal skills, it is as if he is not a data scientist at all. Because if you spend years researching, collecting statistics, programming, and… but can not provide them to the company, your effort is certainly in vain.

A data expert or scientist should be able to talk to teams like the good advertising and marketing team, understand their needs (what part of the data collection do they need?) And then take action, and finally the findings Present yourself well.


In order for the company to grow and sell more, for example, if the company’s customers are only middle-aged people, the data scientist should also offer a way to attract young people, and according to other collected statistics, understand why only middle-aged people like the company’s products and inform managers. Give.

The final step in teaching Data Science with Python

Like other sciences, specializing in data science requires perseverance and effort. You must learn several different sciences, be up to date and be aware of technology and statistics. You do not need to be very intelligent to understand how much the world needs data specialists now.

Just think a little, can accurate, analyzed, and instantaneous statistics not grow a business hundreds of times over? Doesn’t a full understanding of customers’ personalities and habits mean profit? Isn’t the path to continue clear if the information received from everything turns into insight and awareness? And it is not possible to predict the market situation in the future?

book introduction

If you want to become an expert, start now. This article is a complete explanation of data science training with Python. But you need complete courses and many days to learn to complete the learning. Here are some other good books you can read to learn data science.

1.Automate the boring stuff

This book has a simple structure and covers most of the basic concepts about using Python for data science. Including flow control, functions, web scraping, working with json and csv files and bringing programs to better understand is a great book to get started with Python, as well as step-by-step instructions for each technique at the end of each There are also many questions and exercises in the chapter.

Download book:


1.Automate the boring stuff

2.Think stats

A real review of statistics for data science. This book uses data from the National Institutes of Health across the United States to explain the basic concepts of statistics and probabilities needed for data science and analysis. It is a very useful book and contains a lot of examples of Python code and simple programs to explain the concepts. This book is smaller than the theoretical textbooks you may find on the subject, and its teaching style is suggested.

Download the book:


2.Think stats

3.Python data science handbook

A truly comprehensive guide to Python data science from beginner to advanced concepts is available in this book. This book covers things like iPython shell, Pi name library, data manipulation with Pandas, visualization techniques, machine learning. The machine learning chapter is one of the very good chapters of the book.

Download book:


3.Python data science handbook


We hope you find the Data Science article with Python useful.