My own list of helpful resources, interesting projects, and other miscellany, in the tradition of Awesome Python, Awesome Javascript, meta-Awesome, etc.
Contents:
- Google Tech Dev Guide - Lots of foundational CS resources
- Problem Solving with Algorithms and Data Structures using Python - Python-based course with interactive console
- Teach Yourself CS - Overview of important categories, books, and video lectures
- Interactive Coding Challenges - Jupyter Notebook-based coding challenges on algorithms and data structures
- Coding Interview University - Huge list of resources someone put together for their self-study for a Google interview
- Algorithms and Data Structures in Python - Minimal examples
- Visualgo - Visualized algorithms and data structures
- Refactoring Guru - Design Patterns
- You Don't Know JS - Online book; covers ES6
- Eloquent Javascript - Ebook with interactive console
- AirBnB Javascript Style Guide
- Feather - Open source icons
- Callback Hell - An intro to asynchronous Javascript, and some helpful patterns
- Animated Chrome Dev Tools Features
- Adobe Color Wheel
- Simple React Patterns
- Python OOP Tutorials - Working with Classes - Great Youtube series by Corey Schafer
- Python Design Patterns - Design Patterns (mostly based on Gang of Four) implemented in Python
- Hitchhiker's Guide to Python - Kenneth Reitz et al. guide to various topics of Python development
- Built-in Super Heroes - Fun talk about built-in types by Dave Beazley from PyData 2016
- PyFormat - Useful resource about string formatting
- Modern Pandas - Good tutorial on Pandas (more up to date than Wes McKinney's original book). Notebooks to go with.
- Python Data Science Handbook - Set of introductory notebooks on numerical stack by Jake Vanderplas
- Learn Python the Hard Way - Well known book with general Python lessons. Includes some helpful intermediate concepts, like composition vs. inheritance.
- Python Textbook chapter on OOP - Nice Python basics textbook; good chapter on object-oriented programming, patterns, composition, etc.
- Flask by Example - A very helpful walkthrough of Flask/Heroku/SQLAlchemy/Alembic back-end
- How Flask, Heroku, and Alembic play together
- Explore Flask - Free Flask textbook
- From Python to Numpy - Really nice explanations of vectorization and in-depth Numpy examples
- Top-Down machine learning for software engineers
- Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found - Medium post
- Every Programmer Should Know
- Best Practices for Web API development
- Big list of public APIs
- Stack on a budget - list of free/cheap deployment services
- Free-for-dev - more free-tier services
Just a dump of resources and links:
- All the slide decks from PyCon
- https://github.com/nd1/pycon_2017
- https://hynek.me/talks/reliability/
- http://hypothesis.works/articles/what-is-hypothesis/
- https://github.com/sixty-north/cosmic-ray
- http://lcamtuf.coredump.cx/afl/
- https://rollbar.com/
- https://twitter.com/sanacodes/status/865703341786972160
- https://twitter.com/mxmoss/status/865703669110497280
- https://twitter.com/LyonesGamer/status/865701057933918209
- http://intro2017.trey.io/
- http://matthewrocklin.com/slides/pycon-2017.html#/
- https://github.com/kscottz/PythonFromSpace
- https://speakerdeck.com/erikrose/constructive-code-review
- https://archive.org/details/pycon-2017-looping
- https://github.com/plamere/spotipy
- https://github.com/markkohdev/spotify-api-starter
- https://www.divio.com/en/blog/documentation/
- https://github.com/jonathanslenders/python-prompt-toolkit
- https://docs.google.com/presentation/d/1_yTCAiAdWlSZdVgaXlm7qjBiG-Jpi592KgnI5eRZREQ/edit?usp=sharing
- http://python.apichecklist.com/
- http://bit.ly/abstraction-talk
- https://www.youtube.com/watch?v=npw4s1QTmPg
- http://langa.pl/random/talks/unicode.pdf
- https://github.com/sversh/pycon2017-optimizing-pandas