Mastering Health Data Science Using R
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Mastering Health Data Science Using R
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.
Towards Integrative Machine Learning and Knowledge Extraction
The BIRS Workshop “Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets” (15w2181), held in July 2015 in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of “hot topics” toward tackling the grand challenge of reaching a level of useful and useable computational intelligence with a focus on real-world problems, such as in the health domain. This encompasses learning from prior data, extracting and discovering knowledge, generalizing the results, fighting the curse of dimensionality, and ultimately disentangling the underlying explanatory factors in complex data, i.e., to make sense of data within the context of the application domain. The workshop aimed to contribute advancements in promising novel areas such as at the intersection of machine learning and topological data analysis. History has shown that most often the overlapping areas at intersections of seemingly disparate fields are key for the stimulation of new insights and further advances. This is particularly true for the extremely broad field of machine learning.
Beginner's Guide to R Programming
Discover the world of data analysis with "Beginner's Guide to R Programming." This comprehensive resource is crafted to help individuals learn the R programming language and explore its diverse applications. Whether you're a complete beginner or an experienced analyst, our book offers a structured learning path that starts with the basics and progresses to advanced topics like statistical analysis, data visualization, and machine learning. Each chapter includes practical examples, exercises, and real-world case studies, encouraging hands-on experimentation with R code. You'll delve into data types, functions, data manipulation, statistical analysis, data visualization, and more, building a solid foundation in R programming and data analysis. Complex concepts are explained in clear, easy-to-understand language, with visual aids, code snippets, and step-by-step tutorials to help you grasp key ideas effectively. The book emphasizes practical applications of R in real-world scenarios, showcasing how you can use R to solve problems, analyze data, make informed decisions, and communicate insights. With access to supplementary resources, including downloadable datasets, code samples, and additional exercises, you'll further enhance your learning experience and practice your skills.