3 credits
Spring 2025 Lecture Upper Division
This course presents foundational data science methods for analysis of complex biological datasets encountered in biomedical engineering research and applications. After a brief (1-2 weeks) introduction and review of mathematical concepts necessary for data science, the course will cover representative areas of regression, supervised machine learning, unsupervised machine learning, model evaluation, and uncertainty quantification. Assignments and exams will focus on practical examples spanning basic science, engineering, and medical applications.
Learning Outcomes1Identify the appropriate statistical tests to analyze data fitting a given probability distribution.
2Propose an analysis plan for large datasets that tests a hypothesis or answers an exploratory analysis question.
3Implement the appropriate computational model for their analysis plan and interpret the model results with appropriate consideration of uncertainty.
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made by Purdue students.