Data Science from Scratch: First Principles with Python - Practical

Our review of Data Science from Scratch: First Principles with Python finds it best suited for readers who want to understand how data science tools work under the hood rather than just use black-box libraries. Joel Grus presents algorithms and techniques by implementing them from scratch, so the single biggest reason to buy is the learning-by-building approach that forces readers to confront the math and mechanics behind common methods. Updated for Python 3.6 and expanded with material on deep learning and natural language processing, this edition is a hands-on bridge from programming to core data science ideas.

Key Features

• From-scratch implementations: Each algorithm is implemented in plain Python so readers see the step-by-step mechanics behind common data science methods.
• Updated for Python 3.6: Code and examples use modern Python so the material fits current development environments.
• Math and statistics focus: The book helps readers become comfortable with the underlying math and statistics needed to reason about algorithms.
• Expanded topics: New chapters on deep learning and natural language processing expose readers to contemporary areas within data science.
• Practical hacking skills: The text emphasizes the coding and problem-solving skills you need to start doing real data work.

Who It's For

Aspiring data scientists with some programming experience and an aptitude for mathematics will get the most from this book; its hands-on code-first approach is ideal for learners who want to understand why algorithms behave as they do. Readers who appreciate concise, concept-driven exposition will find it a solid companion to practice projects.

However, those seeking a quick cookbook of high-level library usage or a gentle nontechnical introduction should look elsewhere; the book assumes some mathematical comfort and expects readers to work through implementations rather than rely on ready-made toolkits.

Pros & Cons

Pros

• Clear, concept-driven explanations that make core ideas accessible to motivated readers.
• Hands-on code examples that teach the mechanics by building algorithms from scratch.
• Updated coverage that adds deep learning and natural language processing topics relevant to current practice.

Cons

• Pacing can feel brisk for readers without a solid math or programming background, so some may struggle with certain sections.

Specifications

Title	Data Science from Scratch: First Principles with Python
Author	Joel Grus
Python version	Updated for Python 3.6
Approach	Implementations from scratch in Python
Topics added	Deep learning, statistics, natural language processing
Audience	Readers with programming skills and math aptitude

Our Verdict

Data Science from Scratch is a worthwhile investment for motivated learners who want to understand algorithms by building them. It delivers clear explanations and practical code that teach foundational math and programming techniques, making it good value for readers prepared to work through implementations rather than relying on prebuilt libraries.

Frequently Asked Questions

Is prior Python experience required?
Yes; basic programming skills are expected because the book focuses on implementing algorithms in Python.

Does it cover modern topics?
Yes; this edition adds material on deep learning and natural language processing alongside statistics and core algorithms.

Is it a library usage guide?
No; the emphasis is on building methods from scratch to teach underlying principles rather than on library tutorials.