Statistics for Data Scientists An Introduction to Probability, Statistics, and Data Analysis /
Kaptein, Maurits.
Statistics for Data Scientists An Introduction to Probability, Statistics, and Data Analysis / [electronic resource] : by Maurits Kaptein, Edwin van den Heuvel. - 1st ed. 2022. - XXIV, 321 p. 53 illus., 19 illus. in color. online resource. - Undergraduate Topics in Computer Science, 2197-1781 . - Undergraduate Topics in Computer Science, .
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.
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.
9783030105310
10.1007/978-3-030-10531-0 doi
Computer science--Mathematics.
Mathematical statistics.
Statistics .
Probabilities.
Probability and Statistics in Computer Science.
Statistical Theory and Methods.
Probability Theory.
QA76.9.M35 QA276-280
004.0151
Statistics for Data Scientists An Introduction to Probability, Statistics, and Data Analysis / [electronic resource] : by Maurits Kaptein, Edwin van den Heuvel. - 1st ed. 2022. - XXIV, 321 p. 53 illus., 19 illus. in color. online resource. - Undergraduate Topics in Computer Science, 2197-1781 . - Undergraduate Topics in Computer Science, .
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.
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.
9783030105310
10.1007/978-3-030-10531-0 doi
Computer science--Mathematics.
Mathematical statistics.
Statistics .
Probabilities.
Probability and Statistics in Computer Science.
Statistical Theory and Methods.
Probability Theory.
QA76.9.M35 QA276-280
004.0151