Affordable and Informative Textbooks
Health Informatics: Practical Guide
Health Informatics: Practical Guide is in its seventh edition and now has Dr. William Hersh as Co-Editor and Author. It was published in June 2018 and because it is self-published it is very current, unlike many books published by commercial publishers.
There are 22 chapters covering multiple informatics topics of interest to informatics professionals.
The goal of this book is to provide a practical overview of the field of biomedical and health informatics (BMHI), sometimes called health informatics (HI), which is the discipline focused on the use of information, aided by technology, to improve individual health, healthcare, public health, and research in biomedicine and health. The field is sometimes defined as the activity at the intersection of people, information, and technology.
Introduction to Biomedical Data Science
Introduction to Biomedical Data Science was launched in December 2019. Dr. Hoyt was fortunate to find excellent authors and a Co-Editor Bob Muenchen who is an expert in R programming and biostatistics.
There are 11 chapters covering topics from spreadsheets to artificial intelligence. YouTube videos are included in most chapters along with data exercises.
Data Preparation and Exploration: Applied to Healthcare Data
Data Preparation and Exploration: Applied to Healthcare Data was written for anyone who has to prepare, clean, explore and visualize data. The techniques discussed include spreadsheets, statistical packages and programming languages.
This textbook, a companion to Introduction to Biomedical Data Science, tackles the most time-consuming and critical part of a data science project: preparing and exploring data. It provides a practical, hands-on guide for anyone who works with data, offering methods for cleaning, visualizing, and organizing raw datasets. The book uses medical datasets and is well-referenced, with over 100 citations.
Key Features and Content
The book covers essential techniques for data preparation and exploration using a variety of tools, including spreadsheets, statistics packages like Jamovi and BlueSky Statistics, and programming languages like R and Python. It addresses common challenges such as handling missing data, imbalanced datasets, duplicates, and outliers. Readers will learn about descriptive statistics, data visualization, scaling, and standardization. The content is designed to be accessible to beginners, and it is supplemented with helpful YouTube videos to provide additional context and guidance.
A dedicated chapter on health data resources enables readers to find and work with real-world data on their own. The book strikes a balance between theory and practice, providing both the “why” and the “how” behind data science fundamentals. This work, along with its companion volume, reflects the extensive experience of the author, Dr. Hoyt, and his mission to make data science accessible to all practitioners.