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 naive 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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Professional Practice & Development
Illustrationen
79 s/w Abbildungen
79 Illustrations, black and white
Maße
Höhe: 254 mm
Breite: 178 mm
Dicke: 29 mm
Gewicht
ISBN-13
978-1-4822-3481-7 (9781482234817)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
Autor*in
University of California, Berkeley, USA
University of California, Davis, USA
Data Manipulation and Modeling. Simulation Studies. Data- and Web-Technologies. Index.
