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How do you prepare your Python data science projects for production? What are the essential tools and techniques to make your code reproducible, organized, and testable? This week on the show, Khuyen Tran from CodeCut discusses her new book, "Production Ready Data Science".
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Khuyen shares how she got into blogging and what motivated her to write a book. She shares tips on how to create repeatable workflows. We delve into modern Python tools that will help you bring your projects to production.
Topics:
- 00:00:00 -- Introduction
- 00:01:27 -- Recent article about top 6 visualization libraries
- 00:02:19 -- How long have you been blogging?
- 00:03:55 -- What do you cover in your book?
- 00:07:07 -- Potential issues with notebooks
- 00:11:40 -- Structuring data science projects
- 00:15:12 -- Reproducibility and sharing notebooks
- 00:20:33 -- Using Polars
- 00:26:03 -- Advantages of marimo notebooks
- 00:34:21 -- Video Course Spotlight
- 00:35:44 -- Shipping a project in data science
- 00:42:10 -- Advice on testing
- 00:49:50 -- Creating importable parameter values
- 00:53:55 -- Seeing the commit diff of a notebook
- 00:55:12 -- What are you excited about in the world of Python?
- 00:56:04 -- What do you want to learn next?
- 00:56:52 -- What's the best way to follow your work online?
- 00:58:28 -- Thanks and goodbye
👉 Links from the show:
Want to keep learning Python? Explore these free resources:
📘 Python Tutorials →
🛤 Guided Learning →
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2025/11/14
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