Data science in R: a case studies approach to computational reasoning and problem solving

Table of Contents:

Part I. Data Manipulation and Modeling
1.Predicting Location via Indoor Positioning Systems
2.Modeling Runners’ Times in the Cherry Blossom Race
3.Using Statistics to Identify Spam
4.Processing Robot and Sensor Log Files: Seeking a Circular Target
5.Strategies for Analyzing a 12 Gigabyte Data Set: Airline Flight Delays

Part II.Simulation Studies
6.Pairs Trading
7.Simulation Study of a Branching Process
8.A Self-Organizing Dynamic System with a Phase Transition
9.Simulating Blackjack

Part III.Data- and Web-Technologies
10.Baseball: Exploring Data in a Relational Database
11.CIA Factbook Mashup
12.Exploring Data Science Jobs with Web Scraping and Text Mining

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

(https://www.crcpress.com/Data-Science-in-R-A-Case-Studies-Approach-to-Computational-Reasoning-and/Nolan-Lang/9781482234817)