Introduction: What is Data Science? Visualizing Data, matplotlib, Bar Charts, Line Charts, Scatterplots.

Linear Algebra: Vectors, Matrices.

Statistics: Describing a Single Set of Data, Correlation, Simpson’s Paradox, Some Other Correlational Caveats, Correlation and Causation.

Probability: Dependence and Independence, Conditional Probability, Bayes’s Theorem, Random Variables, Continuous Distributions, The Normal Distribution, The Central Limit Theorem.

Hypothesis and Inference: Statistical Hypothesis Testing, Example: Flipping a Coin, p-Values, Confidence Intervals, p-Hacking, Example: Running an A/B Test, Bayesian Inference.

Gradient Descent: The Idea Behind Gradient Descent Estimating the Gradient, Using the Gradient, Choosing the Right Step Size, Using Gradient Descent to Fit Models, Minibatch and Stochastic Gradient Descent.

Getting Data: stdin and stdout, Reading Files, Scraping the Web, Using APIs, Example: Using the Twitter APIs.

Working with Data: Exploring Your Data, Using NamedTuples, Dataclasses, Cleaning and Munging, Manipulating Data, Rescaling, An Aside: tqdm, Dimensionality Reduction.

Machine Learning: Modeling, What Is Machine Learning?, Overfitting and Underfitting, Correctness, The Bias-Variance Tradeoff, Feature Extraction and Selection.

k-Nearest Neighbors: The Model, Example: The Iris Dataset, The Curse of Dimensionality.

Naive Bayes: A Really Dumb Spam Filter, A More Sophisticated Spam Filter, Implementation, Testing Our Model, Using Our Model.

Simple Linear Regression: The Model, Using Gradient Descent, Maximum Likelihood Estimation.

Multiple Regression: The Model, Further Assumptions of the Least Squares Model, Fitting the Model, Interpreting the Model, Goodness of Fit, Digression: The Bootstrap, Standard Errors of Regression Coefficients, Regularization.

Logistic Regression: The Problem, The Logistic Function, Applying the Model, Goodness of Fit, Support Vector Machines.

Decision Trees: What Is a Decision Tree?, Entropy, The Entropy of a Partition, Creating a Decision Tree, Putting It All Together, Random Forests.

Neural Networks: Perceptrons, Feed-Forward Neural Networks, Backpropagation, Example: Fizz Buzz.

Deep Learning: The Tensor, The Layer Abstraction, The Linear Layer, Neural Networks as a Sequence of Layers, Loss and Optimization, Example: XOR Revisited, Other Activation Functions, Example: Fizz Buzz Revisited, Softmaxes and Cross-Entropy, Dropout, Example: MNIST, Saving and Loading Models.

Clustering: The Idea, The Model, Example: Meetups, Choosing k, Example: Clustering Colors, Bottom-Up Hierarchical Clustering.

Natural Language Processing: Word Clouds, n-Gram Language Models, Grammars, An Aside: Gibbs Sampling, Topic Modeling, Word Vectors, Recurrent Neural Networks, Example: Using a Character-Level RNN.

Network Analysis: Betweenness Centrality, Eigenvector Centrality, Directed Graphs and PageRank

Recommender Systems: Manual Curation, Recommending What’s Popular, User-Based Collaborative Filtering, Item-Based Collaborative Filtering, Matrix Factorization.