Modern Data Science with R

Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton (Autoren)

Buch | Hardcover

556 Seiten

2017
Productivity Press (Verlag)
978-1-4987-2448-7 (ISBN)

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Modern Data Science with R

Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton

2021

Buch | Hardcover

CHF 148, ³⁵

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Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions.

Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.

Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research. Daniel T. Kaplan is the DeWitt Wallace professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing, and received the 2006 Macalester Excellence in Teaching award. Nicholas J. Horton is a professor of statistics at Amherst College. He is a Fellow of the American Statistical Association (ASA), member of the NRC Committee on Applied and Theoretical Statistics, recipient of a number of national teaching awards, author of a series of books on statistical computing, and actively involved in curricular reform to help students "think with data."

This site includes additional resources:
http://mdsr-book.github.io/

Introduction to Data Science

Prologue: Why data science?

Data visualization

A grammar for graphics

Data wrangling

Tidy data and iteration

Professional Ethics

Statistics and Modeling

Statistical foundations

Statistical learning and predictive analytics

Unsupervised learning

Simulation

Topics in Data Science

Interactive data graphics

Database querying using SQL

Database administration

Working with spatial data

Text as data

Network science

Epilogue: Towards /big data"

Appendices

Packages used in this book

Introduction to R and RStudio

Algorithmic thinking

Reproducible analysis and workflow

Regression modeling

Setting up a database server

Erscheinungsdatum	25.05.2016
Reihe/Serie	Chapman & Hall/CRC Texts in Statistical Science
Verlagsort	Portland
Sprache	englisch
Maße	178 x 254 mm
Gewicht	1406 g
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Mathematik / Informatik ► Mathematik
	Technik ► Elektrotechnik / Energietechnik
	Wirtschaft ► Allgemeines / Lexika
ISBN-10	1-4987-2448-5 / 1498724485
ISBN-13	978-1-4987-2448-7 / 9781498724487
Zustand	Neuware