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Reading Materials

Here is a curated list of reading materials to help one reinforce the knowledge learned from the open book.

Class references

Assistive tools

  • pythontutor is a powerful tool to help one visually explore every step of Python interpreter. You can see current instruction pointer, variable values and function call stacks. This Youtube video shows you how it works. (Recommended by Kevin Xu)

Beginner exercises

DataCamp provides a good set of multiple choice questions to get you familiar with the language: Link

Data Camp

Online classes

  • ★★★★★ Python Basics class notes and sample codes from ZHANG Honglun. This is good supplementary materials to the week00-week03 in this book. There are abundant exercises to get one familiar with this language. You are strongly suggested to study this repo after we finish week03 if not study earlier.
  • ★★★★☆ Google's Python class This one is organised in a way suitable for developers who have experience in another language or who already have learned Python and use it to strengthen the knowledge. The best time to study this one is before the whole class here, or after you learned week00-week04 (after we cover file operations and HTTP requests/ responses). This study material provides you another perspective of approaching Python. It is always good to have two or more different narratives when you learn a new programming concept.

Related GitHub repos

  • ★★☆☆☆ Project based learning for Python -- a curated list by @tuvtran that includes practical projects you can make with Python. This repo is good for people who have mastered week00-week04 of this open book.
  • ★★★☆☆ A collection of Python Q/A translated from Stackoverflow. This gitbook is good for people who have mastered week00-week03.
  • ★★★☆☆ A collection of interview questions about Python. This may be overshooting for the intended audience of our current repo. The interview questions are mainly for programmer positions. Still, it is good to test how far you can push. You are suggested to read this repo after having enough exercises, e.g. after week12. You can use the language feature section to enhance your understanding of the language basics. For people interested in "algorithm", the last section includes some entry level exercises.
  • ★★★☆☆ Python machine learning notebooks. This repo is recommended for readers who have finished the whole book and would like to learn more about machine learning.

TODO: Feel free to propose your entry into this list

Other resources

Note: following resources are not fully reviewed and evaluated by our team. Those may be good further reading resources but may be far reaching for the intended audience of this open book.

  • Artificial Intelligence in Python, by Prof. Ikhlaq Sidhu. Open source slides and Jupyter notebooks https://github.com/ikhlaqsidhu/data-x . Also see more on: https://data-x.blog
  • List of Python resources (awesome-list style): Link
  • A Python data science book targeted for non-tech background reader. This is a suggested reading for those who master our weekly notes. Link.