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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