Why this book?
- It covers the most important methods and tools used in data exploration, predictive analytics and causal analysis.
- It covers relatively few methods but help students gain a working knowledge and deep intuitive understanding of each method.
- It includes very few formulae and relegates derivations and proofs to appendices or outside resources.
- It gives advice on what method to use when, acknowledging that there is usually no single best solution but data analysts need to make many conscientious decisions.
- It focuses on real-life applications to prepare students to carry out data analysis on their own.
- It explains how to start working with data, including advice on organizing, exploring and describing data.
- It includes many applications that start with raw data so students gain experience in working from scratch.
- Its companion website includes data and code that reproduce all tables and graphs in the textbook. All code is provided both in R and Stata.
- It includes topics of important in practice, such as data quality, methods of data collection, doing experiments.