PUBLIC HEALTH DATA SCIENCE WITH R: USING STATISTICAL ANALYSIS AND MACHINE LEARNING TO UNDERSTAND POPULATION HEALTH AND DISEASE PATTERNS (THE APPLIED DATA SCIENCE WITH R SERIES)

PUBLIC HEALTH DATA SCIENCE WITH R: Using Statistical Analysis and Machine Learning to Understand Population Health and Disease PatternsPublic health decisions today are increasingly driven by data. Governments, hospitals, and research institutions collect massive amounts of health information from disease surveillance systems and national health surveys to electronic medical records and environmental monitoring databases. However, the real value of this data lies in the ability to analyze it effectively and transform it into insights that improve population health.Public Health Data Science with R provides a practical and comprehensive guide to analyzing health data using statistical methods and machine learning techniques in the R programming environment. The book is designed to help readers understand how modern data science tools can be applied to study disease patterns, identify health risks, and support evidence-based public health decisions.This book introduces readers to the essential foundations of public health data analysis and gradually builds toward advanced analytical techniques. It explains how to work with real-world health datasets, clean and prepare health data, explore population health trends, and build predictive models that estimate disease risk. By combining epidemiological principles with modern data science methods, readers will learn how to uncover meaningful insights that inform healthcare planning and policy development.Readers will learn how to:• Set up an efficient R environment for public health data analysis• Import, clean, and prepare healthcare and population health datasets• Conduct exploratory data analysis to identify disease patterns and trends• Create clear and informative data visualizations for health insights• Apply statistical methods to analyze risk factors and health outcomes• Build machine learning models to predict disease risks• Analyze epidemiological data for disease surveillance and outbreak detection• Develop a complete public health data science project from data collection to reportingThe book also includes practical charts, conceptual diagrams, and analytical examples that help readers understand how public health data analysis works in real-world scenarios. These visual explanations simplify complex analytical concepts and make the learning process easier for both beginners and experienced analysts.This book is ideal for:• Public health professionals and epidemiologists• Data scientists interested in healthcare analytics• Biostatistics and health informatics students• Healthcare researchers and analysts• Anyone interested in using R to analyze health data and disease patternsUnlike many technical books that focus solely on programming syntax, this guide emphasizes real-world public health applications of data science. It demonstrates how analytical techniques can be used to answer critical health questions, detect emerging disease trends, and support better health policy decisions.As healthcare systems continue to generate increasing amounts of data, professionals who can analyze and interpret this information will play a vital role in improving public health outcomes. By learning how to combine statistical analysis, machine learning, and epidemiological reasoning using R, readers will gain the skills needed to transform complex health data into meaningful insights that can guide future public health strategies. Read more