A Biostatistics Short Course
A six-week short course for researchers or students wanting to learn more about data science, machine learning, data visualization, and tools for reproducible research and team science. Three lectures provide an introduction to machine learning (ML) in biomedical research, covering topics such as: differences between ML and classical statistical models, the basic steps for building and validating predictive models (“supervised ML”), tools for interpreting ML models, variable selection, and “unsupervised ML” (e.g. “clustering methods” to derive patient subgroups, and principal component analysis for “dimension reduction”).
Schedule of Topics
- Overview of Data Science
- Introduction of Machine Learning in Biomedical Research
- Clustering and Pattern Finding ("unsupervised" Machine Learning)
- Powerful Predictive Models and Variable Selection ("supervised" Machine Learning)
- Fundamentals of Data Visualization
- Reproducible Research and Team Science
This introductory short course does not replace formal education in biostatistics. Continuous attendance is encouraged. In-class and online participation is required. A certificate of completion will be provided with 5 out of 6 in-class lectures attended.
Course Fee: $340
Location: This course will be virtual.
A credit card can be used for payment. To inquire about using a Speedtype, please contact firstname.lastname@example.org.
Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.