Data Science From Scratch

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)