This is the code repo for the O'Reilly book "Data Science: The Hard Parts" to be released in September 2023.

If you're new to python, I recommend downloading the Anaconda Distribution. Please follow the instructions thereby provided.

All code is provided as chapter-specific Jupyter notebooks (download instructions here) that are rendered on Github.

• Chapter 1: So What. Creating Value With Data Science
• Chapter 2: Metrics Design
• Chapter 3: Growth Decompositions: Understanding Tail and Headwinds
• Chapter 4: 2x2 Designs
• Chapter 5: Building Business Cases
• Chapter 6: What's In a Lift?
• Chapter 7: Narratives
• Chapter 8: Datavis: Choosing the Right Plot to Deliver a Message

• Chapter 9: Simulation and Bootstrapping
• Chapter 10: Linear Regression: Going Back to Basics
• Chapter 11: Data Leakage
• Chapter 12: Productionizing Models
• Chapter 13: Storytelling in ML
• Chapter 14: From Prediction to Decisions
• Chapter 15: Incrementality: The Holy Grail of Data Science?
• Chapter 16: A/B Tests
• Chapter 17: Large Language Models and the Practice of Data Science

I recently started writing blog posts on my substack. You can find the accompanying code here.