Book Summary: 101 Data Science Drawings

101 Data Science Drawings by Raymond Lim is a visually-driven study aid covering over 100 core topics in data science, including machine learning, statistics, SQL, econometrics, and career advice. Each topic is presented as a colorful hand-drawn infographic accompanied by a short caption and, where available, a social video walkthrough.

Who This Book Is For

Data science students preparing for interviews or exams
Researchers and analysts who want visual intuition for statistical methods
Development practitioners using quantitative methods in M&E, policy analysis, or programme evaluation
Anyone who learns better from diagrams than from equations

Structure

The book is organised into 6 parts:

Part 1: Supervised Learning

Models that learn from labelled data. Covers linear and logistic regression, decision trees, random forests, gradient boosting, SVMs, neural networks, and key concepts like bias-variance tradeoff and cross-validation.

Part 2: Unsupervised Learning

Finding patterns without labels. Covers K-means clustering, hierarchical clustering, PCA, and dimensionality reduction techniques.

Part 3: Probability & Statistics

The mathematical foundations. Covers distributions, hypothesis testing, confidence intervals, Bayesian thinking, and common statistical tests.

Part 4: Econometrics

Causal inference methods used in economics and social science. Covers OLS regression, instrumental variables, difference-in-differences, regression discontinuity, and propensity score matching.

Part 5: SQL

Querying and manipulating data. Covers joins, aggregations, subqueries, window functions (LAG, LEAD, RANK), and common table expressions (CTEs).

Part 6: Career

Practical advice for data science job seekers. Covers portfolio building, interview preparation, communication skills, and navigating the job market.

Companion Materials

Resource	Description
Key Concepts	Definitions and context for core terms
Applications	How concepts apply in real-world work
Visual Walkthroughs	Video explainers from @MinuteData
Further Resources	Books, courses, tools, and related repos