Название: Statistics for Data Scientists: An Introduction to Probability, Statistics, and Data AnalysisАвтор: Maurits Kaptein, Edwin van den Heuvel
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2022
Страниц: 341
Язык: английский
Формат: pdf (true)
Размер: 14.8 MB
This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for Data Science.
This book was designed to support a single semester course for undergraduate students of data science. We used the material in the first year of the Data Science program, placed in the curriculum just after basic calculus and a programming course. However, we have since used this material also for teaching other students: for computer science, artificial intelligence, and econometrics students. We have used this material during their undergraduate programs to introduce them to modern statistical methods and the relevant probability theory. For social science students (psychology, sociology), we have used this material even at the master’s level; for students with a little mathematical background and no programming experience, the material in this book provides a challenging next step in their capacity for dealing with data. Next to these targeted students, we feel this book is relevant for anyone with limited knowledge of statistics, but some familiarity with basic mathematics and programming, who wants to not just gain procedural knowledge of statistical inference, but properly understand the basic principles and its modern applications.
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