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PHYS 440 Big Data Physics 3.0 Credits

This course provides the framework for physics students at all levels to begin interacting with large data sets in physics. Data analysis will be done using Python tools, including standard libraries for machine learning. Practical application of classification and regression techniques for both unsupervised and supervised data are emphasized, in addition to dimensionality reduction techniques and time-domain analysis. An introduction to statistical methods, Bayesian inference, and Markov-chain Monte Carlo methods provide a foundation for application of machine learning tools.

College/Department: College of Arts and Sciences
Repeat Status: Not repeatable for credit
Restrictions: Cannot enroll if classification is Freshman
Prerequisites: PHYS 115 [Min Grade: D] or CS 171 [Min Grade: D]

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