Essential Statistics for Data Science: A Concise Crash Course, Mu Zhu

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.

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