Data Science Fundamentals and Machine Learning

Not open to students who are taking or who have received credit for MATH 342W. Recommended corequisites include ECON 382, 387, MATH 341, MATH 343 or their equivalents. Philosophy of modeling with data. Prediction via linear models and machine learning including support vector machines and random forests. Probability estimation and asymmetric costs. Underfitting vs. overfitting and model validation. Formal instruction of data manipulation, visualization and statistical computing in a modern language. Prereq: A course in linear algebra, a course in probability, and a course in programming (CSCI 111 or the equivalent).

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