Data Science in R : A Case Studies Approach to Computational Reasoning and Problem Solving

This book examines the drivers for improved performance of the drinking water sector in Ghana by focussing on the role of the national macro-environment, water supply related organisations and institutions and the water service providers (urban and small towns). Political interference, which adversely affects the drinking water sector in both the urban and the small towns'' water supply are described. The drinking water sector is gradually implementing aspects of the New Public Management (NPM) with the small towns'' water sector ,ore advanced than the urban water sector. Water pricing in the dinking water sector is analysed and its particular implication on the urban poor is highlighted. The role of the policy, legal and regulatory environment in achieving universal service coverage in sustainable, effective, equitable and efficient manner is presented.

Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData 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 messagesText processing and regular expressionsNewer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google EarthStatistical methods, such as classification trees, k-nearest neighbors, and na BayesVisualization and exploratory data analysisRelational databases and Structured Query Language (SQL)SimulationAlgorithm implementationLarge data and efficiencySuitable 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.