Python course for data analysis

The following course is based on Python 3.8, but some interesting features available on newer versions are occasionally mentioned throughout the tutorial

This course is meant for beginners but also experienced programmers that wish to expand their knowledge

No prior programming experience is needed but some basic notions of mathematics are recommended

Most modules include exercises and their solutions, so don't miss the chance to put things into practice!

What is Python and what is it used for?

Python is a high-level, object-oriented programming language that works on many platforms (Linux, macOS, Windows, Raspberry Pi, etc). It's easy to learn and can pretty much be used for anything, some examples are:

• Web development
• Videogame development
• AI & Machine Learning
• Data analytics

Python in a nutshell

Everything in Python is an object of an specific type or class. Objects contain data in the form of attributes and code in the form of methods. Objects are instances of classes, which also determine their types.

Objects are assigned to variables. A variable is a symbolic name that is a reference (or pointer) to an object, but the data itself is still contained within the object.

Course's content

This course is divided into six notebooks/modules (which can be found in the 'Jupyter-Notebooks/' folder) targeting different aspects:

1. Introduction to Python for data analysis: Basics
2. Introduction to Python for data analysis: Functions
3. Introduction to Python for data analysis: Object-oriented programming
4. Introduction to Python for data analysis: NumPy and Pandas
5. Introduction to Python for data analysis: Data Visualization
6. Introduction to Python for data analysis: Helpful tips and modules

For those who wish to learn more about Python, there is an extension of this course that is composed of different Python modules targetting aspects not covered on the main course (which can be found in the 'Python-tutorial-extension/' folder: link).

If you like this course, please consider giving me a star!

How to start?

There are two ways in which you could take this course:

Using Google Colab (recommended)

I strongly recommend using Google Colab to be able to play and code throughout the course without having to setup anything in your computer. This way, you will be able to run all the notebooks yourself and do the exercises directly in the notebook as well. For this option, follow the links above under "Course's content".

Locally

If you wish to run the Jupyter notebooks in your laptop/computer, please follow the instructions below to install Python, Jupyter, and download the content of the course.

How to install Python (Linux, Windows, macOS)?

Follow the instructions here.

How to install Jupyter notebook (Linux, Windows, macOS)?

Linux

Follow the instructions here.

Windows

Follow the instructions here.

macOS

Follow the instructions here.

How to download the course (notebooks and extra material)?

If you are already familiar with Git, you could clone the repository, if not, follow these instructions instead:

• At the top of this page, you can see a green button (left to the About section) saying <> Code, click it and then clik on Download ZIP. Below you can find instructions on how to unzip the downloaded file on every OS.

How to unzip downloaded file on Linux?

Follow the instructions here.

How to unzip downloaded file on Windows?

Follow the instructions here.

How to unzip downloaded file on macOS?

Follow the instructions here.