Hands-On Data Science with R

Good data science is more than choosing an algorithm: it means gathering and preparing data, understanding what it can tell you, and selecting methods that fit the problem. Hands-On Data Science with R follows that broader process, pairing R techniques with examples that move from statistical foundations and data preparation toward predictive models and larger-scale analytics.

Follow the work from raw data to useful analysis

The book opens with an introduction to data science and R, then establishes a statistical foundation through measures of central tendency and dispersion, hypothesis testing, t-tests, z-tests, and an introduction to A/B testing. From there, it turns to the practical work that often occupies much of an analysis: importing, cleaning, transforming, joining, and checking data.

Make R’s data tools work together

Readers encounter base R alongside packages and tools including dplyr, tidyr,, and dbplyr. The coverage extends to web data, APIs, text mining, and exploratory techniques such as visualization and clustering. This breadth helps place individual R functions in the context of a complete analytical workflow.

From exploration to predictive models

Later chapters explore machine learning methods such as regression, tree models, random forests, support vector machines, clustering, and neural networks. The book also covers forecasting, building applications with Shiny, interactive visualization, and working with Spark and Hadoop for large-scale data analytics.

A practical reference for R-based data science

Examples and applied use cases give the material a hands-on orientation. The range—from statistical summaries and data-quality checks to predictive modeling and distributed analytics—makes the book relevant to readers who want to connect R techniques rather than study them in isolation.

Who will find it useful?

The publisher identifies data analysts, data engineers, statisticians, and R programmers interested in machine learning and predictive analytics as its intended readers. Some R programming experience is required; a basic understanding of statistics and linear algebra is also helpful.