|Course ID||Course Name||Semester||Time | Day|
|HGEN 612||Data Science II||Fall||10:00-11:20 | WF|
Prerequisite: HGEN 611/OVPR 611. This course builds upon the material introduced in the prerequisite course by providing instruction on advanced techniques for working with data and producing highly reproducible data products. The learning path focuses on the fundamentals of both machine learning and the creation of production-ready web applications as two highly marketable skills for future data scientists. Project-based assignments culminate in students creating their own applications that take advantage of tidymodel principles to automate machine-learning workflows, visually communicate knowledge with interactive graphics and using Git and OSF for project management. The guiding principle of the course is that the these products of research should be open and accessible to all members of a project team for maximum impact. This course will continue the use of the statistical programming language R with a focus on advanced tidyverse functions for data wrangling and statistical model development. Crosslisted as: OVPR 612.
Terms: Fall | Credits 3 | Grading: A-F
Schedule for HGEN 612
August thru December || Wed, Fri 10:00 AM – 11:20 AM
Timothy York, Ph.D.
My interests are in the application and development of statistical genetic methodology to quantify the contribution of genetic and environmental sources to complex traits, human disease and development.