BMI713.0

Computational Statistics for Biomedical Sciences

Credits: 4.00 CREDITS (Required)
Directors: Park, Peter J.
Prerequisites: Permission of course director. Maximum enrollment: 26 students
Offered: Fall
Time: TBA
Location: TBA
Open to Exclerks: No (HMS only)
Description:
This course will provide a practical introduction to analysis of biological and biomedical data. Basic statistical techniques will be covered, including descriptive statistics, elements of probability, hypothesis testing, nonparametric methods, correlation analysis, and linear regression. Emphasis will be on how to choose appropriate statistical tests and how to assess statistical significance. To visualize data and carry out statistical testing, students will learn R, a powerful programming language for statistical computing and graphics. No previous knowledge in statistics or programming is required, although those with no programming experience will be expected to devote a significant amount of extra time. The class will be a combination of lecture and computer lab. This course is geared toward graduate students, but postdoctoral fellows and others are welcomed as space allows.
Learning Goals:
Understanding of descriptive statistics, elements of probability, hypothesis testing, nonparametric methods, correlation analysis, and linear regression

Understanding of how to choose appropriate statistical tests and how to assess statistical significance.

Understanding of how to visualize data and carry out statistical testing.

Understanding of R, a powerful programming language for statistical computing and graphics.
Incorporation of Basic Science Content and Evidence-Based Medicine:
The course, Computational Statistics for Biomedical Sciences, will provide a practical introduction to analysis of biological and biomedical data.
Grade Criteria:
This is a graded course.

Completion of all problem sets. Regular attendance. Regular participation in class discussion.
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