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**(Bayesâ€™ 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)