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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