Data Science in R

Auteur
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.

Texte du rabat

This book explains 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, exercises, and sample solutions encompass practical topics pertaining to data processing and analysis. The book can be used for self-study or as supplementary reading in a statistical computing course, allowing students to gain valuable data science skills.

Résumé

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 naïve 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.

Contenu
Data Manipulation and Modeling. Simulation Studies. Data- and Web-Technologies. Index.