Bio 5075 Fundamentals of Biostatistics for Graduate Students
The ability to quantitatively evaluate one’s data is increasingly important in scientific research. Yet many entering PhD students lack a fundamental understanding of the statistical principles and basic programming skills that are needed to rigorously and reproducibly analyze modern biomedical data. This one-credit course is a primer on fundamental statistical and computational skills and concepts for first through third year DBBS students; it assumes no prior experience in statistics or programming. The course will cover common statistical practices and concepts in the life sciences, such as summary statistics, probability distributions, simulation and hypothesis testing, and power analysis for experimental design. In parallel, the class will teach core Python programming skills required to perform these statistical computations.
The course format includes lectures and in-class computational activities, and emphasizes practical problem-solving skills by teaching both core statistical concepts and computational methods to implement them. Upon completing the course, students will be able to retrieve and analyze simple and genomic-style datasets from online databases, write simple data analysis scripts in Python, create the major types of statistical plots, critically evaluate experimental designs, and use simulation methods to summarize and assess the significance of their data.
Instructors: Zach Pincus and Mike White, Department of Genetics
Teaching Assistants: Aidan Schneider, Yawei Wu
Live Zoom sessions are held Mondays from 2:30 – 4:00 pm. Zoom link is available through the Canvas course page or from the instructors upon request.