Population Health: Responsible Data Analysis
Master data analysis for population health decisions. Learn to gather, clean, and analyze health data responsibly.
Master data analysis for population health decisions. Learn to gather, clean, and analyze health data responsibly.
Dive into responsible data analysis for population health management. This course equips you with skills to extract valuable insights from health data, covering data collection, privacy, statistical inference, and regression techniques. Learn to interpret results critically, understand bias, and report findings accurately. Gain practical experience using R for analysis, all within the context of making informed decisions about population health. Ideal for health professionals and data enthusiasts alike, this course balances conceptual understanding with hands-on application.
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What you'll learn
Understand the importance of responsible data management and initial data analysis in population health
Learn to choose and apply appropriate statistical methods for health-related problems
Develop skills in interpreting statistical results and drawing responsible conclusions
Master the basics of R programming for data analysis in health contexts
Gain proficiency in regression techniques, including linear, logistic, and Cox proportional hazards regression
Understand the challenges and ethical considerations in big data analysis for population health
Skills you'll gain
This course includes:
1.9 Hours PreRecorded video
22 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course on responsible data analysis for population health covers essential topics in data management, statistical analysis, and ethical considerations. Students will learn how to collect, clean, and explore health data while addressing privacy concerns. The course delves into statistical inference, hypothesis testing, and various regression techniques, providing a solid foundation for interpreting complex health data. Emphasis is placed on critical assessment of results and understanding the challenges of data analysis in the era of big data. Practical skills in R programming are developed throughout the course, enabling students to apply their knowledge to real-world population health scenarios.
Welcome to Responsible Data Analysis
Module 1 · 46 Minutes to complete
From Individuals to Data
Module 2 · 9 Hours to complete
From data to information I: statistical inference
Module 3 · 4 Hours to complete
From data to information II: regression techniques
Module 4 · 7 Hours to complete
From information to knowledge
Module 5 · 5 Hours to complete
Fee Structure
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Instructors
Assistant Professor in Biostatistics at Universiteit Leiden
Mar Rodríguez-Girondo, PhD, is an assistant professor in biostatistics at the Department of Biomedical Data Sciences at the Leiden University Medical Centre. Passionate about teaching, Mar emphasizes innovative, responsible, and scientifically sound data analysis methods. She has extensive practical experience in diverse multidisciplinary projects, including integrating high-dimensional omic data and utilizing historical demographic family studies to explore longevity. Mar is particularly interested in balancing theory and practice to effectively disseminate a responsible approach to data analysis. She teaches the course Population Health: Responsible Data Analysis, which equips students with essential skills for conducting ethical and effective data analyses in public health contexts.
Professor of Biostatistics at Universiteit Leiden
Jelle Goeman is a Professor of Biostatistics at the Leiden University Medical Center, where he specializes in high-dimensional data analysis, particularly in the fields of omics and neuroimaging. His research includes the development of innovative statistical methods for gene set testing, lasso regression, and multiple testing corrections, with a focus on post-selection inference. Jelle is dedicated to advancing the field of biostatistics through his contributions to methodology and application in biomedical research. He teaches the course Population Health: Responsible Data Analysis, which emphasizes ethical and effective data analysis practices in public health. Through this course, he aims to equip students with the skills necessary to analyze complex data responsibly and apply their findings to real-world health challenges.
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4.5 course rating
40 ratings
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