Gather Data Science In R: A Case Studies Approach To Computational Reasoning And Problem Solving Fabricated By Deborah Nolan Textbook
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 Data Science in R's collection of projects, comprehensive sample solutions, and followup exercises encompass practical topics pertaining to data processing, including:
Nonstandard, 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, knearest 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 selfstudy 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 realworld data analyses, Exactly what the title says: case studies in data analysis using the R statistical computing environment,
Unlike most collections of casestudies, the cases here are wellchosen, and complement each other to highlight different analytic techniques, Many that appear dry and uninformative e, g. , simulation of a branching process actually prove to be the most interesting due to the stepwiserefinement approach taken by the authors, Each case begins with a "Computational Topics" section which lists the R programming techniques that will be covered in detail,
The contributed chapters are the weakest which is surprising, given that one of the contributors is Hadley Wickham, whose own books are excellent and seem to lack the bigpicture vision of the primary authors.
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