Introduction to data science and machine learning, with case studies in discovery of structure-property relationships and new materials from experimental and computational data. Brief review of required background in linear algebra and statistics with hands-on exercises in Python. Data science topics: model fitting, clustering, dimensionality reduction, ontologies, Bayesian inference, and design of experiments.
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