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.”

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.

Course Spotlight: Python Project Management With uv

Create and manage Python projects with uv, a blazing-fast package and project manager built in Rust. Learn setup, workflow, and best practices.

Topics:

00:00:00 – Introduction
00:01:27 – Recent article about top six 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

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