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MEDI504 is a Special Topics course designed to develop literacy and conceptual understanding of data science to enable effective collaboration with data scientists as related to health research spanning the basic, clinical, population and health services.
Learning Outcomes. This course targets students who are interested in how medical care can be transformed by “Big data”. The instructors will use real-life clinical use cases to motivate how clinical care can be enhanced by collecting unconventional data of various kinds and feeding such data to machine learning methods to build next-generation clinical tools. However, even with the appropriate data collected, the teaching team will describe the data science challenges that need to be overcome before the data can be used properly. This course will introduce the main concepts and methods in biostatistics, and machine learning, including unsupervised and supervised methods, and data visualization. Last but not least, there will be discussion of ethical issues, fairness issues, and other potential pitfalls of applying machine learning to medical care.
The four major learning outcomes for the students are:
- Understand the fundamental concepts of machine learning and predictive modeling.
- Apply data science processes and project management techniques to biomedical datasets.
- Perform exploratory data analysis (EDA) and create user stories for data science projects.
- Develop and evaluate logistic regression models and other machine learning techniques.