Here are the chapters from the book Data Science from Scratch by Joel Grus.

Blue Indicates I have played around with these chapters.

**Chapter 1: Introduction** (What is data science?)
**Chapter 2: A Crash Course in Python** (syntax, data structures, control flow, and other features)
**Chapter 3: Visualizing Data** (bar, line and scatter plots with matplotlib)
**Chapter 4: Linear Algebra** (vectors and matricies)
**Chapter 5: Statistics** (central tendency and correlations)
**Chapter 6: Probability** (Bayes’ Theorem, Random Variables, Normality)
**Chapter 7: Hypothesis and Inference** (confidence intervals, P values, Bayesian inference)
**Chapter 8: Gradient Descent** (gradients, steps, stochastic variation)
**Chapter 9: Getting Data** (scraping HTML, JSON APIs)
**Chapter 10: Working with Data** (basic viz, data transforms)
**Chapter 11: Machine Learning** (fitting, bias-variance, feature selection)

**Chapter 12: k-Nearest Neighbors** (also curse of dimensionality)
**Chapter 13: Naive Bayes**
**Chapter 14: Simple Linear Regression** (also gradient descent)
**Chapter 15: Multiple Regression** (also bootstrap, regularization)
**Chapter 16: Logistic Regression** (also SVM)
**Chapter 17: Decision Trees** (also random forest)
**Chapter 18: Neural Networks** (perceptron and back-prop)
**Chapter 19: Clustering** (k-Means)
**Chapter 20: Natural Language Processing** (n-gram, grammars, Gibbs sampling)

**Chapter 21: Network Analysis** (Centrality and PageRank)
**Chapter 22: Recommender Systems** (user- and item-based)
**Chapter 23: Databases and SQL** (basic usage)
**Chapter 24: MapReduce** (various worked examples)
**Chapter 25: Go Forth and Do Data Science** (libs you should use)

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