Today we’re turning tiny tips into big wins. Khuyen Tran, creator of CodeCut.ai, has shipped hundreds of bite-size Python and data science snippets across four years. We dig into open-source tools you can use right now, cleaner workflows, and why notebooks and scripts don’t have to be enemies. If you want faster insights with fewer yak-shaves, this one’s packed with takeaways you can apply before lunch. Let’s get into it.

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Links from the show

Khuyen Tran (LinkedIn): linkedin.com
Khuyen Tran (GitHub): github.com

CodeCut: codecut.ai
Production-ready Data Science Book (discount code TalkPython): codecut.ai

Why UV Might Be All You Need: codecut.ai
How to Structure a Data Science Project for Readability and Transparency: codecut.ai
Stop Hard-coding: Use Configuration Files Instead: codecut.ai
Simplify Your Python Logging with Loguru: codecut.ai
Git for Data Scientists: Learn Git Through Practical Examples: codecut.ai
Marimo (A Modern Notebook for Reproducible Data Science): codecut.ai
Text Similarity & Fuzzy Matching Guide: codecut.ai
Loguru (Python logging made simple): github.com
Hydra: hydra.cc
Marimo: marimo.io
Quarto: quarto.org
Show Your Work! Book: austinkleon.com

Watch this episode on YouTube: youtube.com
Episode #522 deep-dive: talkpython.fm/522
Episode transcripts: talkpython.fm

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#522: Data Sci Tips and Tricks from CodeCut.ai

Talk Python To Me

Notebooks vs Scripts: Tradeoffs and Best Practices

Links from the show

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