Modeling Data in the Tidyverse
Master data modeling in R using tidyverse tools, from hypothesis testing to machine learning. Learn statistical analysis and predictive modeling techniques.
Master data modeling in R using tidyverse tools, from hypothesis testing to machine learning. Learn statistical analysis and predictive modeling techniques.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Tidyverse Skills for Data Science in R Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Describe and apply different types of data analytic questions
Conduct effective hypothesis tests of data
Implement linear modeling techniques for multivariable analysis
Apply machine learning workflows for pattern detection
Develop prediction models using tidymodels
Skills you'll gain
This course includes:
11.5 Hours PreRecorded video
8 quizzes, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 11 modules in this course
This comprehensive course covers data modeling using the tidyverse ecosystem in R. Students learn different types of data analytic questions, hypothesis testing, linear modeling techniques, and machine learning workflows. The curriculum includes both theoretical concepts and practical applications using packages like tidymodels, with emphasis on real-world data analysis scenarios and predictive modeling.
Modeling Data Basics
Module 1 · 3 Hours to complete
Inference
Module 2 · 1 Hours to complete
Linear Modeling
Module 3 · 2 Hours to complete
Multiple Linear Regression
Module 4 · 45 Minutes to complete
Beyond Linear Regression
Module 5 · 23 Minutes to complete
Hypothesis Testing
Module 6 · 1 Hours to complete
Prediction Modeling
Module 7 · 2 Hours to complete
The tidymodels Ecosystem
Module 8 · 2 Hours to complete
Case Studies
Module 9 · 5 Hours to complete
Summary of tidymodels
Module 10 · 5 Minutes to complete
Project: Modeling Data in the Tidyverse
Module 11 · 1 Hours to complete
Fee Structure
Instructors
Pioneering Data Science Education and Computational Biology Innovation
Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.
Professor of Biostatistics at Johns Hopkins University
Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and serves as a Co-Editor of the Simply Statistics blog. He earned his PhD in Statistics from the University of California, Los Angeles, and is recognized for his research in air pollution, health risk assessment, and statistical methods for environmental data. In 2016, he received the Mortimer Spiegelman Award from the American Public Health Association, honoring his significant contributions to health statistics. Dr. Peng developed the Statistical Programming course at Johns Hopkins to equip students with essential computational tools for data analysis. Additionally, he is a national leader in promoting reproducible research practices and serves as the Reproducible Research editor for the journal Biostatistics. His interdisciplinary research has been published in prestigious journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. He has authored over a dozen software packages that implement statistical methods for environmental studies and reproducible research, and he regularly conducts workshops and tutorials on statistical computing and data analysis.
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