Data science is being used in many ways to improve healthcare and reduce costs. We have written a textbook, Introduction to Biomedical Data Science, to help healthcare professionals understand the topic and to work more effectively with data scientists. The textbook content and data exercises do not require programming skills or higher math. We introduce open source tools such as R and Python, as well as easy-to-use interfaces to them such as BlueSky Statistics, jamovi, R Commander, and Orange. Chapter exercises are based on healthcare data, and supplemental YouTube videos are available in most chapters.

For instructors, we provide PowerPoint slides for each chapter, exercises, quiz questions, and solutions. Instructors can download an electronic copy of the book, the Instructor Manual, and PowerPoints after first registering on the instructor page.

The book is available in print
and various electronic formats
. Because it is self-published, we plan to update it more rapidly than would be

of the textbook authors.

​OVERVIEW OF BIOMEDICAL DATA SCIENCE

1. Introduction
2. Background and history
3. Conflicting perspectives
1. the statistician’s perspective
2. the machine learner’s perspective
4. the data visualizer’s perspective
4. Data analytical processes
1. raw data
2. data pre-processing
3. exploratory data analysis (EDA)
4. predictive modeling approaches
5. types of models
6. types of software
5. Major types of analytics
1. descriptive analytics
2. diagnostic analytics
3. predictive analytics (modeling)
4. prescriptive analytics
5. putting it all together
6. Biomedical data science tools
7. Biomedical data science education
8. Biomedical data science careers
9. Importance of soft skills in data science
10. Biomedical data science resources
11. Biomedical data science challenges
12. Future trends
13. Conclusion
14. References

1. Introduction
2. Navigating the worksheet
1. formulas and functions
2. filter
3. sorting data
4. freezing panes
5. conditional formatting
6. pivot tables
7. visualization
8. data analysis
4. Tips and tricks
1. Microsoft Excel shortcuts – windows users
2. Google sheets tips and tricks
5. Conclusions
6. Exercises
7. References

​​BIOSTATISTICS PRIMER

1. Introduction
2. Measures of central tendency & dispersion
1. the normal and log-normal distributions
3. Descriptive and inferential statistics
4. Categorical data analysis
5. Diagnostic tests
6. Bayes’ theorem
7. Types of research studies
1. observational studies
2. interventional studies
3. meta-analysis
4. orrelation
8. Linear regression
9. Comparing two groups
1. the independent-samples t-test
2. the wilcoxon-mann-whitney test
10. Comparing more than two groups
11. Other types of tests
1. generalized tests
2. exact or permutation tests
3. bootstrap or resampling tests
12. Stats packages and online calculators
1. commercial packages
2. non-commercial or open source packages
3. online calculators
13. Challenges
14. Future trends
15. Conclusion
16. Exercises
17. References

​​DATA VISUALIZATION

1. Introduction
1. historical data visualizations
2. visualization frameworks
2. Visualization basics
3. Data visualization software
1. Microsoft Excel
3. Tableau
4. R programming language
5. other visualization programs
4. Visualization options
1. visualizing categorical data
2. visualizing continuous data
5. Dashboards
6. Geographic maps
7. Challenges
8. Conclusion
9. Exercises
10. References

​​INTRODUCTION TO DATABASES

1. Introduction
2. Definitions
3. A brief history of database models
1. hierarchical model
2. network model
3. relational model
4. Relational database structure
5. Clinical data warehouses (CDWs)
6. Structured query language (SQL)
7. Learning SQL
8. Conclusion
9. Exercises
10. References

BIG DATA

1. Introduction
2. The seven v’s of big data related to health care data
3. Technical background
4. Application
5. Challenges
1. technical
2. organizational
3. legal
4. translational
6. Future trends
7. Conclusion
8. References

​​BIOINFORMATICS and PRECISION MEDICINE

1. Introduction
2. History
3. Definitions
4. Biological data analysis – from data to discovery
5. Biological data types
1. genomics
2. transcriptomics
3. proteomics
4. bioinformatics data in public repositories
5. biomedical cancer data portals
6. Tools for analyzing bioinformatics data
1. command line tools
2. web-based tools
7. Genomic data analysis
8. Genomic data analysis workflow
1. variant calling pipeline for whole exome sequencing data
2. quality check
3. alignment
4. variant calling
5. variant filtering and annotation
6. downstream analysis
7. reporting and visualization
9. Precision medicine – from big data to patient care
10. Examples of precision medicine
11. Challenges
12. Future trends
13. Useful resources
14. Conclusion
15. Exercises
16. References

​​PROGRAMMING LANGUAGES FOR DATA ANALYSIS

1. Introduction
2. History
3. R language
1. installing R & rstudio
2. an example R program
3. getting help in R
4. user interfaces for R
5. R’s default user interface: rgui
6. Rstudio
8. some popular R guis
9. R graphical user interface comparison
10. R resources
4. Python language
1. installing Python
2. an example Python program
3. getting help in Python
4. user interfaces for Python
5. reproducibility
6. R vs. Python
7. Future trends
8. Conclusion
9. Exercises
10. References

​​MACHINE LEARNING

1. Brief history
2. Introduction
1. data refresher
2. training vs test data
3. bias and variance
4. supervised and unsupervised learning
3. Common machine learning algorithms
4. Supervised learning
5. Unsupervised learning
1. dimensionality reduction
2. reinforcement learning
3. semi-supervised learning
6. Evaluation of predictive analytical performance
1. classification model evaluation
2. regression model evaluation
7. Machine learning software
1. Weka
2. Orange
3. Rapidminer studio
4. KNIME
6. honorable mention
7. summary
8. Programming languages and machine learning
9. Machine learning challenges
10. Machine learning examples
1. example 1 classification
2. example 2 regression
3. example 3 clustering
4. example 4 association rules
11. Conclusion
12. Exercises
13. References

​​ARTIFICIAL INTELLIGENCE

1. Introduction
1. definitions
2. History
3. Ai architectures
4. Deep learning
5. Image analysis (computer vision)
2. Ophthalmology
3. Dermatology
4. Pathology
5. Cardiology
6. Neurology
7. Wearable devices
8. Image libraries and packages
6. Natural language processing
1. NLP libraries and packages
2. Text mining and medicine
3. Speech recognition
7. Electronic health record data and AI
8. Genomic analysis
9. AI platforms
1. deep learning platforms and programs
10. Artificial intelligence challenges
1. General
2. Data issues
3. Technical
4. Socio economic and legal
5. Regulatory
7. Need for more ML and AI education
11. Future trends
12. Conclusion
13. Exercises
14. References

## Authors

Brenda Griffith
Technical Writer
Data.World
Austin, TX

Robert Hoyt MD, FACP, ABPM-CI, FAMIA
Associate Clinical Professor
Department of Internal Medicine
Virginia Commonwealth University
Richmond, VA

David Hurwitz MD, FACP, ABPM-CI
Associate CMIO
Allscripts Healthcare Solutions
Chicago, IL

Bioinformatics
Washington University at St. Louis, School of Medicine
St. Louis, MO

Robert Leviton MD, MPH, FACEP, ABPM-CI, FAMIA
Assistant Professor
New York Medical College
Department of Emergency Medicine
Valhalla, NY

Karen A. Monsen PhD, RN, FAMIA, FAAN
Professor
School of Nursing
University of Minnesota
Minneapolis, MN

Robert Muenchen MS, PSTAT
Manager, Research Computing Support
University of Tennessee
Knoxville, TN

Dallas Snider PhD
Chair, Department of Information Technology
University of West Florida
Pensacola, FL

​A special thanks to Ann Yoshihashi MD for her help with the publication of this textbook.