Название: Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving Автор: Deborah Nolan, Duncan Temple Lang
Издательство: Chapman and Hall/CRC
Серия: The R Series
ISBN: 1482234815
Год: 2015
Страниц: 539
Язык: английский
Формат: pdf (true)
Размер: 15.9 MB
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.
There are many different languages people commonly use to do data analysis and data science. We focus primarily on R, but also use several other domain specific languages (DSLs) and even touch on languages such as the UNIX shell and C. This book is not intended to teach the syntax or semantics of the R language, or any of the other languages we use. Nor is it written to list the large number of packages and functions that data scientists commonly use in R. Instead, we wrote the book so that people could experience the thought process involved in solving authentic computational problems related to data analysis problems.
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?ve 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.
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