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MODERN DATA SCIENCE WITH R
Título:
MODERN DATA SCIENCE WITH R
Subtítulo:
Autor:
BAUMER, B
Editorial:
CRC PRESS
Año de edición:
2017
Materia
ESTADISTICA
ISBN:
978-1-4987-2448-7
Páginas:
556
84,50 €

 

Sinopsis

Features

Offers a text accessible to a general audience with some background in statistics and computing
Includes many examples and extended case studies
Contains illustrations using R and Rstudio
Provides a true blend of statistics and computer science -- not just a grab bag of topics from each
This site includes additional resources:

http://mdsr-book.github.io/

Summary

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.



Table of Contents


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