Coming soon · Book Profile
Data Science from Scratch: First Principles with Python
A hands-on introduction to data science that teaches the core concepts, algorithms, and mathematics by implementing everything from scratch in Python rather than relying on existing libraries.
A profile of this book is on the way.
What it’s about
Data Science from Scratch teaches you data science by having you build the tools and algorithms yourself, from the ground up, in clear and readable Python. Rather than treating libraries like NumPy, scikit-learn, and pandas as magic black boxes, Joel Grus walks you through implementing linear algebra, statistics, probability, gradient descent, machine learning models, neural networks, deep learning, clustering, NLP, network analysis, and recommender systems by hand. Framed around the fictional 'DataSciencester' social network, the book grounds abstract concepts in concrete problems while developing the hacking skills and mathematical intuition that are at the core of doing real data science. By the end you understand not just how to use data science tools, but how and why they work.
The through-line
- Who it’s for
- An aspiring data scientist with some mathematical aptitude and programming skill who wants to genuinely understand how data science works, not just call library functions.
- The problem
- They need to learn the core algorithms, mathematics, and tools of data science well enough to actually do the work. They feel like an underachiever or impostor who can use libraries but doesn't truly understand what's happening under the hood.
- The plan
- Get comfortable with Python and the language features that matter for data science.
- Build a foundation in linear algebra, statistics, and probability.
- Learn to get, clean, explore, and manipulate real data.
- Implement core machine learning models and evaluation techniques from scratch.
- Advance to neural networks, deep learning, clustering, NLP, and recommender systems.
- The payoff
- You possess a solid understanding of the fundamentals of data science. · You can build, train, and evaluate models while understanding how they work. · You can confidently use production libraries because you know what they do under the hood.
See our guide
Related profiles we’ve built
- A Practical Guide To Conjoint Analysis →
- Analytics at Work: Smarter Decisions, Better Results →
- Big Data: A Revolution That Will Transform How We Live, Work, and Think →
- Big Data_ A Very Short Introduction (Very Short Introductions) →
- Business Adventures →
- Business Intelligence Guidebook: From Data Integration to Analytics →
- Data Mining for Business Analytics: Concepts, Techniques, and Applications →
- Data Warehouse and Data Mining →
Additional reading
- Competing on Analytics: The New Science of Winning · Thomas H. Davenport and Jeanne G. Harris
The authors' previous book, which provides the strategic context by describing the earliest and most aggressive adopters of analytics. This book builds on it by providing a 'how-to' guide for all organizations.
- Sources of Power: How People Make Decisions · Gary Klein
Discusses decision-making in high-pressure situations where there is no time for systematic data gathering, providing a contrast to the analytical approach and showing when intuition is necessary.
- The Black Swan: The Impact of the Highly Improbable · Nassim Nicholas Taleb
Argues that statistical analysis is limited because it cannot predict rare, high-impact 'black swan' events, serving as a cautionary note on the limits of analytics.
- Moneyball: The Art of Winning an Unfair Game · Michael Lewis
A popular case study of how the Oakland A's baseball team used an analytical approach to player selection to compete with richer teams, illustrating the power of competing on analytics.
- Why Great Leaders Don't Take Yes for an Answer · Michael Roberto
Describes how to foster a culture of constructive conflict and debate in decision-making processes, which is essential for an analytical culture where assumptions are tested and merit triumphs over politics.
- The Visual Display of Quantitative Information · Edward Tufte
A foundational work on how to create clear visual representations of data, a key skill for communicating analytical findings effectively.
- Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart · Ian Ayres
The book discusses how statistical analyses are replacing human intuition and expert judgment in decision-making, a core theme related to the discussion of 'Moneyball' and the demise of the expert.
- Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed · James Scott
Documents how governments' fetish for quantification and data can lead to misguided and harmful policies, providing a deep historical context for the book's warnings about the 'dictatorship of data'.
- The War Managers · Douglas Kinnard
A survey of U.S. generals' views on the Vietnam War, revealing that the 'body count' metric was seen as a worthless and inflated measure of progress, illustrating the dangers of relying on flawed data.
- Thinking, Fast and Slow · Daniel Kahneman
Explains the cognitive biases that lead humans to see illusory causal links, which the author's argue big data correlations can challenge and disprove.