Hi! And welcome to my Python for Analysts training course!
I originally wrote this a couple of years ago when I was first starting out with Python and my career as a data scientist. I had come from a background of SAS and Excel and once I'd taken Jose Portilla's excellent Udemy Course I was blown away by how easy, intuitive and powerful Python was for data analaysis and data science and wanted to create my own training course both so I could share the awesomeness of Python with others, and also as a means to document and share my learning.
After two years in the wilderness I've decided to come back to it and update the repo, both in terms of the original content (some of which I've re-written!) and also to add the stuff I've learned in the interim. This is still a work in progress but having drafted a few chapters on ML, I'm very much back into it and am looking forward to adding to it over the course of the next few weeks and months.
I've split up the original repo (which was a wall of Jupyter Notebooks) into separate folders as follows:
- basics
- data_analysis
- data_viz
- machine_learning
- other
Presently the 5. other folder contains a mixture of stuff that will be refactored into new sections of the training as I go. For a detailed breakdown of what's included in each of the folders, check out the individual README.md files (most of which aren't written yet!)
I've added a github project to log all the stuff I'm planning also, so feel free to check that out.
Massive thanks to Emma Beynon for her work on the Statistics and Machine Learning notebooks and also for her help in QA'ing and delivering this.