Teaching

Master’s level course that mirrors the entire data science life cycle in practice, including problem formulation, data cleaning, exploratory data analysis, statistical and machine learning modeling and computational techniques, and interpretation of results in context. It is guided by the Predictability-Computability-Stability (PCS) framework for veridical data science and emphasizes critical thinking and documenting human judgment calls and code. It coaches not only the technical but also communication and teamwork skills in order to obtain responsible and reliable data-driven conclusions for solving complex real world problems. Projects include data cleaning and analysis, computer vision autoencoders, and large language models for brain voxel activation prediction.

This course covers both the how and why of reproducible and collaborative research by combining questions of good computational practice in science, open science and statistical data analysis, in the context of today’s research environment. Topics covered include scientifc computing, computational and statistical issues involved with reproducibility, git fundementals, and project documentation.