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Statistics for Data Scientists

An Introduction to Probability, Statistics, and Data Analysis

  • Textbook
  • © 2022

Overview

Authors:
  1. Maurits Kaptein

    1. Tilburg University, Tilburg, The Netherlands

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    You can also search for this author in PubMed   Google Scholar

  2. Edwin van den Heuvel

    1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

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    You can also search for this author in PubMed   Google Scholar

  • Provides an accessible introduction to applied statistics by combining hands-on exercises with mathematical theory
  • Introduces statistical inference in a natural way, using finite samples and real data
  • Contains modern statistical methods including Bayesian decision theory, equivalence testing and statistical modelling

Part of the book series: Undergraduate Topics in Computer Science (UTICS)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xxiv
    Download chapter PDF

  2. A First Look at Data

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 1-37

  3. Sampling Plans and Estimates

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 39-79

  4. Probability Theory

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 81-102

  5. Random Variables and Distributions

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 103-140

  6. Estimation

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 141-169

  7. Multiple Random Variables

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 171-239

  8. Making Decisions in Uncertainty

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 241-285

  9. Bayesian Statistics

    • Maurits Kaptein, Edwin van den Heuvel
    Pages 287-321

  10. Correction to: Statistics for Data Scientists

    • Maurits Kaptein, Edwin van den Heuvel
    Pages C1-C1
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Keywords

About this book

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.

Reviews

“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)

Authors and Affiliations

  • Tilburg University, Tilburg, The Netherlands

    Maurits Kaptein

  • Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Edwin van den Heuvel

About the authors

Prof. Dr. Maurits Kaptein works on statistical methods for sequential experimentation. He has extensive experience in research and education in the fields of statistics, machine learning, and research methodology. Maurits works for the Jheronimus Academy of Data Science and for the University of Tilburg. His work has been published in influential journals such as Bayesian Analysis and the Journal of Interactive Marketing.



Prof. Dr. Edwin van den Heuvel works on statistical methods for analyzing cross-sectional and longitudinal data from experimental and observational studies in the domain of health and life sciences. He has been teaching many different topics on statistics to (PhD, master, and bachelor) students from different backgrounds (medicine, engineering, mathematics, etc.) He is full-time professor in statistics at Eindhoven University of Technology and has affiliations at other universities. He publishes mostly in peer-reviewed influential statistical, epidemiological, and medical journals. 

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