Books

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. Printed copies of this book are available through Lulu.

The book covers all the key skills needed for preparing, exploring, and analysing longitudinal data. To facilitate understanding and help readers learn these skills, it interweaves statistical modelling with computer code and visualizations. It does this using real-world data, code, and outputs that readers can replicate.

This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies. This book is based on the industry-leading Johns Hopkins Data Science Specialization.

Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications and describe the theory as it becomes necessary to solve specific challenges. The book includes links to computer code that readers can use to follow along as they program.

Este manual está dedicado íntegramente a los fundamentos de R y sólo a ellos. Se espera así complementar la formación de los científicos de datos, que disponen de cientos de manuales sobre análisis de datos con R pero apenas cuentan con manuales, completos, que fundamenten en detalle la herramienta a utilizar.

The book provides a modern look at introductory Biostatistical concepts and the associated computational tools using the latest developments in computation and visualization in the R language environment. The book includes practical data analysis based on datasets that can be downloaded here: https://github.com/muschellij2/biostatmethods.

Descubre cómo aprender a ser un Data Scientist a tu propio ritmo con contenido actualizado y con ejemplos de Latinoamérica. Desde análisis de datos hasta algoritmos predicción con machine learning. Con la compra del libro accedes a las 2 versión disponibles: pdf y web y a más de 100 ejercicios incluidos.

This book gives an introduction to several aspects of econometric production analysis. It provides many practical examples using the R statistical software.

This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)

This book gives a brief, but rigorous, treatment of regression models intended for practicing Data Scientists.

This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. This book is based on the Coursera Class "Developing Data Products" as part of the Data Science Specialization. Particular emphasis is paid to developing Shiny apps and interactive graphics.

Develop insights from data with tidy tools. Import, wrangle, visualize, and model data with the Tidyverse R packages.

The Working Notes complement Applied Data Science for Credit Risk and Probability of Default Rating Modeling with R, offering practice-oriented insights. Based on the author’s GitHub repository, they address real-world challenges and are regularly updated to reflect ongoing developments.

Biological Data Science with R covers data manipulation with dplyr, visualization with ggplot2, essential statistics, survival analysis, RNA-seq analysis, phylogenetic trees, predictive modeling and infectious disease forecasting, text mining and natural language processing, and more.

If you are an independent learner or an instructor for a data science, statistics, or public health course, check out the open case studies project (www.opencasestudies.org) and this guide which will describe the variety of ways our case studies can be used for hands-on data science activities.