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:
- Introduction to Python for data analysis: Basics
- Introduction to Python for data analysis: Functions
- Introduction to Python for data analysis: Object-oriented programming
- Introduction to Python for data analysis: NumPy and Pandas
- Introduction to Python for data analysis: Data Visualization
- 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:
Active (recommended)
I strongly recommend using a Jupyter notebook in your laptop to be able to play and code throughout the course. This way, you will be able to do the exercises directly in the notebook and test them to see if they work as intended. Please follow the instructions below to install Jupyter notebook 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 onDownload 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.
Passive
If you only wish to follow the course without putting things into practice, you can just look at the notebooks in the Jupyter-Notebooks/ folder on GitHub* or follow the links above which will render the notebooks through nbviewer which works better than GitHub to display Jupyter notebooks. This way, you will not be able to make any change to the notebooks, nor test anything.
* If you experience problems loading the notebooks on Github, I recommend you to use nbviewer. You can follow the links above or go to nbviewer.org, paste the URL link and press "Go!", and the notebook will be displayed without problems.