Prerequisites: ENGEK103 and EK381. Programming Fundamentals for Biomedical Engineering Data Analysis with Python (BE500) or equivalent programming experience in Python (requires instructor approval). Proficiency in probability, calculus, and linear algebra. - This course will cover the conceptual foundations of data science and introductory machine learning for biomedical engineers and serves as a foundational course in data analytics for students in the biomedical sciences. It is designed to follow in depth-study of math (linear algebra, calculus, and probability) and programming and will prepare students for graduate-level classes focusing on more advanced applications of machine learning and data science. This course, taught in Python, will cover the theory and practical applications of hypothesis testing, model fitting and parameter estimation, classification, clustering, dimensionality reduction, and artificial neural networks.

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