Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall/Crc the R)

Nolan, Deborah; Lang, Duncan Temple

ISBN 10: 1138469297 / ISBN 13: 9781138469297

Published by Chapman and Hall/CRC, 2017

About this Item

Seller Inventory # 26379393461

Bibliographic Details

• Title: Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall/Crc the R)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2017
• Language: English
• ISBN 10: 1138469297
• ISBN 13: 9781138469297
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions.

The book‘s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including:

Non-standard, complex data formats, such as robot logs and email messages
Text processing and regular expressions
Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth
Statistical methods, such as classification trees, k-nearest neighbors, and na Bayes
Visualization and exploratory data analysis
Relational databases and Structured Query Language (SQL)
Simulation
Algorithm implementation
Large data and efficiency

Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data.

Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers computational reasoning of real-world data analyses.

About the Author

Deborah Nolan holds the Zaffaroni Family Chair in Undergraduate Education at the University of California, Berkeley. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research. Duncan Temple Lang is the director of the Data Science Initiative at the University of California, Davis. He has been involved in the development of R and S for 20 years and has developed over 100 R packages. His research focuses on statistical computing, data technologies, meta-computing, reproducibility, and visualization.