Python is one of (if not) the most essential Data Science languages. It’s fairly easy to learn, it’s free, many companies are using it, and it has a tons of powerful statistical and data visualization libraries. In one sentence: if you are looking for a Data Science career, sooner or later you have to learn Python.

So I put together a Python 3 for Data Science tutorial series starting from the very basics. As far as I know, this is one of the few Python tutorials online that’s:

  • in Python 3 and not in Python 2 (see why this is important below)
  • written for those who are just starting with coding
  • 100% dedicated to being practical
  • and free…

Here are the articles!

Note: If you are an intermediate Python learner and want to learn Pandas and other cool Python extensions (like MatPlotLib, ScikitLearn, etc.), check the Learn Pandas and intermediate Python article series!

1) Install Python, SQL, R and Bash (for non-devs)

The very first step will be to set up your own Python 3 environment. This article will show you how to do that. Plus, as an extra, if you go through the whole process, you will have bash, SQL and R too. The setup comes with the famous iPython and Jupyter Notebook Python extensions that will make your data-coding-life much easier! READ>>

2) Python Basics: the environment, Python variables and data types

In this article, I introduce the surface of the Jupyter Notebook, your soon-to-be-favorite interactive Python 3 workspace. After that, we dig into the basics of Python: variables and data types (integers, strings, booleans, etc.). At the end of the episode you will find a quick exercise too! READ>>

3) Python Data Structures

The next article is about the most important data structures in Python 3: lists, dictionaries and tuples. You will learn how to create and modify these – also how to access or update their elements. READ>>

4) Python Built-in Functions and Methods

Functions and methods are the greatest advantages of Python. Using them, you can carry out  simple but important data processes (like counting the number of elements, calculating the sum of integers, making strings upper- or lowercase, and so on…). In this article, I introduce the whole concept and give you a list of the most essential built-in functions and methods of Python 3. READ>>

5) Python 2 vs Python 3

At this point, you have understood the basics of Python for Data Science. It’s time to clarify why we are using Python 3 and not Python 2.  READ>>

6) Python if statements

Let’s get back to coding! The next chapter presents the if statements. You can learn about the logic of Python 3 if statement – as well as the syntax and advanced applications. READ>>

7) Python for loops


More coming soon…


This is a continuously expanding article. So check back time to time – or subscribe to my Newsletter list to get notified about new episodes!

Check the SQL and the bash tutorials, too!

Plus, if you have any questions, let me know in the comment sections below the articles!

Tomi Mester