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 engine : building a user-facing data product -- Data visualization and fraud detection -- Social networks and data journalism -- Causality -- Epidemiology -- Lessons learned from data competitions -- Data engineering -- 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