Description
Taylor and Francis Modern Data Science With R 2017 Edition by Nicholas J. Horton, Benjamin S. Baumer, Daniel T. Kaplan
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. This site includes additional resources:http://mdsr-book.github.io/Introduction to Data SciencePrologue: Why data science?Data visualizationA grammar for graphicsData wranglingTidy data and iterationProfessional EthicsStatistics and ModelingStatistical foundationsStatistical learning and predictive analyticsUnsupervised learningSimulationTopics in Data ScienceInteractive data graphicsDatabase querying using SQLDatabase administrationWorking with spatial dataText as dataNetwork scienceEpilogue: Towards \big data"AppendicesPackages used in this bookIntroduction to R and RStudioAlgorithmic thinkingReproducible analysis and workflowRegression modelingSetting up a database server