2013-10-08: first release ; 2013-12-13: second release ; 2014-10-10: third release.

Includes index.

Introduction : What is data science? -- Statistical inference, exploratory data analysis, and the data science process -- Algorithms -- Spam filters, naive bayes, and wrangling -- Logistic regression -- Time stamps and financial modeling -- Extracting meaning from data -- Recommendation engines : building a user-facing data product at scale -- Data visualization and fraud detection -- Social networks and data journalism -- Causality -- Epidemiology -- Lessons learned from data competitions : data leakage and model evaluation -- Data engineering : MapReduce, Pregel, and Hadoop -- The students speak -- Next-generation data scientists, hubris, and ethics.

A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering.