A five-volume collection of open course notes spanning foundations, data work, machine learning, advanced analytical methods, and selected topics in data science and artificial intelligence.
Each page contains the full-volume PDF, front matter, and chapter-level links.
Volume 1
Programming Primer; Multivariate Calculus for Data Analysis; Overview of Linear Algebra; Basics of Numerical Methods; A Survey of Optimization; Probability and Applications; Introductory Statistical Analysis; Classical Regression Analysis; Time Series and Forecasting; Survey Sampling Methods; The Design of Experiments; Simulations and Modeling.
Volume 2
Non-Technical Aspects of Quantitative and Data Work; Data Science Basics; Data Preparation; Web Scraping and Automatic Data Collection; Data Engineering and Data Management; Data Exploration and Data Visualization.
Volume 3
Introduction to Machine Learning; Regression and Value Estimation; Focus on Classification and Supervised Learning; Focus on Clustering; Feature Selection and Dimension Reduction.
Volume 4
Queueing Systems; Bayesian Data Analysis; Anomaly Detection and Outlier Analysis; Text Analysis and Text Mining; Mining Data Streams.
Volume 5
(Social) Network Data Analysis; What's the Big Deal with Big Data?; A Deep Learning Launchpad; Natural Language Processing; Reinforcement Learning and A.I.; Recommender Engines; Image Analysis and Computer Vision; Causality and Belief Networks.
Companion volume
With J. Schellinck and S. Davies. A complementary volume focused on data graphics, visual communication, and the practice of building effective visual explanations.