Statistics for data scientists : an introduction to probability, statistics, and data analysis / Maurits Kaptein, Edwin van den Heuvel.
2022
QA276.4 .K36 2022
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Details
Title
Statistics for data scientists : an introduction to probability, statistics, and data analysis / Maurits Kaptein, Edwin van den Heuvel.
Author
ISBN
9783030105310 (electronic bk.)
3030105318 (electronic bk.)
9783030105303
303010530X
3030105318 (electronic bk.)
9783030105303
303010530X
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustration (some color).
Item Number
10.1007/978-3-030-10531-0 doi
Call Number
QA276.4 .K36 2022
Dewey Decimal Classification
519.50285
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 8, 2022).
Added Author
Series
Undergraduate topics in computer science.
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Table of Contents
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.
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.