dabeaz-course/python-mastery

Advanced Python Mastery (course by @dabeaz)

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Summary Information

Updated 17 minutes ago
Added to GitGenius on September 4th, 2026
Created on July 17th, 2023
Open Issues & Pull Requests: 2 (+0)
GitHub issues: Enabled
Number of forks: 2,294
Total Stargazers: 13,317 (+1)
Total Subscribers: 99 (+0)

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Median issue/PR response: 23.6 days
Mean response time: 37.1 days
90th percentile: 80.9 days
Tracked items: 4

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Open issues: 1
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Closed in 7 days: 0
Avg open age: 1,133 days
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Detailed Description

Python Mastery is an exercise-driven advanced Python programming course designed to help developers move beyond scripts to writing sophisticated programs.

The course addresses the gap between introductory Python knowledge and the deeper understanding needed to work effectively with popular libraries and frameworks. It teaches programming techniques and language internals through a structured curriculum of hands-on exercises that build progressively. The approach combines presentation slides with practical coding exercises, allowing learners to develop a more complete mental model of how Python works and apply that knowledge to their own projects.

This course suits intermediate Python programmers who want to understand the techniques underlying popular libraries rather than beginners seeking basic syntax. It works best for those with a local development environment who can dedicate significant time to working through exercises sequentially. The course targets the Python 3.6 feature set, so modern language features receive limited coverage. Learners should expect to invest 30 to 50 hours and work through exercises locally using an editor or IDE rather than notebooks, as the material relies on multiple files and module imports.

The course materials show consistent maintenance and updates across its full span. The presentation slides, exercise directories, and solution code are all actively maintained and available for self-directed study. Supplemental materials on specialized topics like generators, coroutines, metaprogramming, and modules are provided as references for deeper exploration of selected subjects.