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Statistics for Data Scientists: An Introduction to Probability, Statistics, and Data Analysis: Undergraduate Topics in Computer Science

Autor Maurits Kaptein, Edwin van den Heuvel
en Limba Engleză Paperback – 3 feb 2022
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.
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Specificații

ISBN-13: 9783030105303
ISBN-10: 303010530X
Pagini: 322
Ilustrații: XXIV, 321 p. 53 illus., 19 illus. in color.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.49 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Undergraduate Topics in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.

Recenzii

“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)

Notă biografică

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. 

Textul de pe ultima copertă

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.

Caracteristici

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