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A Selection Of Python Tutorials for Analysts - Issue 61
A roundup of my favorite tutorials for data analysts to either get started with or upkeep existing Python skills
Hello analysts! As promised earlier, today’s newsletter is all about Python tutorials. I am sure every one of you has your own list of handy videos, courses, and go-to pages for Python solutions and tips. In this issue, I am going to save you countless hours of research and share my favorite list of Python tutorials for data processing, transformations, cleaning, analysis, and visualizations.
A little pre-history: I started my analytical journey with R (like any gray-haired medieval analyst today). And after all that time, I still think R language is the most suited for statistics and analysis. I had to make a switch because every team I was a part of was using Python, and many projects we collaborated on were also done in Python.
My transition from R to Python was … long. I think I went through every Python class out there. After too many tutorials, I started making a list of my favorite go-to videos and lessons which I still use and keep updating to this day. Most of those are (or were) free, but things change, so it is possible some of the classes below are not free anymore.
You probably have noticed already that I’m a fan of Real Python tutorials. I think they have a good combination of theory and examples, covering everything from basics to advanced development. So many of my favorite sources come from there.
Setting up a Python Development Environment in Sublime Text (if anyone is using Sublime)
Pandas, Data Frame, and Data Series - really like this channel. Data frame and series explanations are very good.
Data cleaning and EDA
Cheat Sheet for Exploratory Data Analysis in Python - infographic by Analytics Vidhya
Generating Random Data in Python (not cleaning or EDA, but helpful for testing)
Analysis and some simple ML
Chart Visualization - pandas documentation
Learn Python - Full Course for Beginners - a full complete course (4 hours) of Python Intro.
Python Data Science Handbook - The entire Python Data Science Handbook, in the form of free Jupyter notebooks.
Astronomical Data in Python - the code is written in Jupyter notebooks. You can run the notebooks either on Colab or in your own environment (you can download them from the repository and follow the instructions to set up your environment).
StatQuest with Josh Starmer - less Python and mostly ML.
Ultimate Python study guide - all in one: a good resource and guide.
Python from Nisha M - a good explanation about data types.
I have another list for SQL and Statistics tutorials, which I’ll share soon as well. I continually update this list to keep it fresh and relevant, so please, send me your favorite Python links and sources that you think I should add!
Thanks for reading, everyone. Until next Wednesday!