I am sharing my “work in progress” data science student notebooks. These are my notes and test cases from various classes and projects I’ve been working on. These are built using the Jupyter interactive notebook technology, so you can read, run, revise, and explore.
To share, I have placed my student notebooks on GitHub. I will be adding future notebooks to the collection and will increase the content and readability of the existing notebooks.
I have taken a good number of on-line courses and read a fair number of books on Python and data science. However, I find the only truly effective way to learn a new language and new techniques is to invent new and unique scenarios and work through them myself. These notebooks show the scenarios I created from scratch to practice certain Python features and data science techniques. I certainly welcome you to look over my shoulder, but encourage you to press forward and create your own learning scenarios.
To date, the notebooks are organized as follows:
Python Basic Topics
- Code Formatting
- Iterators, Iterables, and Generators
- Lambdas, map(), and reduce()
- Useful Functions
Python Intermediate Topics
I hope you find these useful!
(Python logo provided courtesy of Python Software Foundation, used here under the “nominative use rules” of their policy.)