Essential Statistics for Data Science: A Concise Crash Course is for students entering a serious graduate program or advanced undergraduate teaching in data science without knowing enough statistics. The three-part text starts from the basics of probability and random variables and guides readers towards relatively advanced topics in both frequentist and Bayesian approaches in a matter of weeks.

Part I, Talking Probability explains that the statistical approach to analysing data starts with a probability model to describe the data generating process. Part II, Doing Statistics explains that much of statistical inference is about learning unknown quantities in the model (e.g. its parameters) from the data it is presumed to have generated. Part III, Facing Uncertainty explains the importance of explicitly describing how much uncertainty we have about the model parameters, especially those with intrinsic scientific meaning, and of taking that into account when making decisions.

Essential Statistics for Data Science: A Concise Crash Course provides an in-depth introduction for beginners, while being more serious than a typical undergraduate text, but still lighter and more accessible than an average graduate text.

Mu Zhu's profile page

Mu Zhu is Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, and Fellow of the American Statistical Association. He received his AB magna cum laude in applied mathematics from Harvard University, and his PhD in statistics from Stanford University. He is currently Director of the Graduate Data Science Program at Waterloo.