Mastering Health Data Science Using R (eBook, ePUB)
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This book provides a practical, application-driven guide to using R for public health and health data science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to ...
This book provides a practical, application-driven guide to using R for public health and health data science, accessible to both beginners and those with some coding experience. Each module starts with data as the driver of analysis before introducing and breaking down the programming concepts needed to tackle the analysis in a step-by-step manner. This book aims to equip readers by offering a practical and approachable programming guide tailored to those in health-related fields. Going beyond simple R examples, the programming principles and skills developed will give readers the ability to apply R skills to their own research needs. Practical case studies in public health are provided throughout to reinforce learning.
Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an introduction to probability, statistical inference and regression analysis.
Key features:
· Includes practical case studies.
· Explains how to write larger programmes.
· Contains additional information on Quarto.
Alice Paul is an Assistant Professor of Biostatistics and Teaching Scholar, holding a Ph.D. in Operations Research from Cornell University. With six years of teaching experience at the undergraduate, master's, and Ph.D. levels, she instructed students in diverse fields, including biostatistics, engineering, computer science, and data science at both Brown University and Olin College of Engineering.
Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, regression analysis, and larger scale programming with functions and control flows. The presentation focuses on implementation with R and assumes readers have had an introduction to probability, statistical inference and regression analysis.
Key features:
· Includes practical case studies.
· Explains how to write larger programmes.
· Contains additional information on Quarto.
Alice Paul is an Assistant Professor of Biostatistics and Teaching Scholar, holding a Ph.D. in Operations Research from Cornell University. With six years of teaching experience at the undergraduate, master's, and Ph.D. levels, she instructed students in diverse fields, including biostatistics, engineering, computer science, and data science at both Brown University and Olin College of Engineering.
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