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Probability for Data Scientists (Paperback)
Loot Price: R3,532
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Probability for Data Scientists (Paperback)
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Probability for Data Scientists provides students with a
mathematically sound yet accessible introduction to the theory and
applications of probability. Students learn how probability theory
supports statistics, data science, and machine learning theory by
enabling scientists to move beyond mere descriptions of data to
inferences about specific populations. The book is divided into two
parts. Part I introduces readers to fundamental definitions,
theorems, and methods within the context of discrete sample spaces.
It addresses the origin of the mathematical study of probability,
main concepts in modern probability theory, univariate and
bivariate discrete probability models, and the multinomial
distribution. Part II builds upon the knowledge imparted in Part I
to present students with corresponding ideas in the context of
continuous sample spaces. It examines models for single and
multiple continuous random variables and the application of
probability theorems in statistics. Probability for Data Scientists
effectively introduces students to key concepts in probability and
demonstrates how a small set of methodologies can be applied to a
plethora of contextually unrelated problems. It is well suited for
courses in statistics, data science, machine learning theory, or
any course with an emphasis in probability. Numerous exercises,
some of which provide R software code to conduct experiments that
illustrate the laws of probability, are provided in each chapter.
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