Henderson Costa's Key Ideas from Data Science from Scratch
by Joel Grus

Python here is not just a programming language — it’s a vehicle for understanding math and logic. You'll code everything manually: dot products, means, correlations, regressions, and more.

One key chapter is about gradient descent. You’ll learn to define loss functions, compute derivatives, and update parameters step by step — just like training a model manually.

Rather than abstracting everything away, this book has you build core algorithms from the ground up:

• Linear regression

• Logistic regression

• k-Nearest Neighbors

• Simple decision trees
• 
  
• Recommendation engines based on similarity

You'll explore concepts like:

• Correlation vs. causation

• Pitfalls of averages

• When to use standard deviation vs. interquartile range

• Hypothesis testing and p-values

Above all, Grus shows that data science is about mental discipline, not tools. Building things yourself forces you to understand deeply and question assumptions.

CS or stats students seeking a deeper foundation

Self-taught learners who don’t want shortcuts

Developers transitioning into data science

Anyone who wants to think like a true data scientist

Data Science from Scratch doesn’t just teach you how to do data science. It teaches you why it works, what it’s built on, and how to think critically at every step.

> Learn it. Code it. Understand it. Build it from scratch.