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.

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.

This book provides a practical guide to critical data science methods, focusing on their application in credit risk management. Using examples in R and Python, it presents step-by-step processes for applying various analytical techniques while highlighting the importance of aligning methods with the specific characteristics of the data. Designed for practitioners and those with foundational data science and banking knowledge, the book bridges theory and practice with real-world examples.

This book teaches the fundamental concepts and tools behind reporting modern data analyses in a reproducible manner. As data analyses become increasingly complex, the need for clear and reproducible report writing is greater than ever. The material for this book was developed as part of the industry-leading Johns Hopkins Data Science Specialization. Printed versions are available through Lulu (see link below).

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.

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.

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!)

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.

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

A rigorous treatment of linear models for self learning data scientists. This book is only available in pdf form.

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.

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

How long will a population persist, and why do some populations go extinct while others survive? 'Predicting Persistence' will help you to get a grasp of the fundamental aspects of the theory underlying these questions, and it will show you how to simulate them with the programming language R.

Discover how to become a Data Scientist at your own pace with updated content and real-world examples. From data analysis to prediction algorithms with machine learning. This Third Edition includes new chapters on Generative AI, Ethics, and modern Machine Learning workflows.