Essential Statistics for Data Science: A Concise Crash Course [Paperback]

Essential Statistics for Data Science: A Concise Crash Courseis for students entering a serious graduate program in data science without knowing enough statistics.Essential Statistics for Data Science: A Concise Crash Courseis 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 Probabilityexplains that the statistical approach to analysing data starts with a probability model to describe the data generating process.Part II, Doing Statisticsexplains 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 Uncertaintyexplains 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 Courseprovides 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.PrologueI Talking Probability1. Eminence of Models1.A. For brave eyes only2. Building Vocabulary2.1. Probability2.1.1 Basic rules2.2. Conditional probability2.2.1 Independence2.2.2 Law of total probability2.2.3 Bayes law2.3. Random variables2.3.1 Summation and integration2.3.2 Expectations and variances2.3.3 Two simple distributions2.4. The bell curve3. Gaining Fluency3.1. Multiple random quantities3.1.1 Higher-dimensional problems3.2. Two hard problems3.2.1 Functions of random variables3.2.2 Compound distributions3.A. SumlcQ