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Mar 25, 2025
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MIST 270: Data Science
Units: 4
Introduces the data analytics pipeline relevant to graduate research work: obtaining raw unstructured data; cleaning, organizing, merging and identifying potential pitfalls in the data; exploring and visualizing the underlying statistics; introduction to preliminary stochastic, generative and econometric modeling methods. Introduces best-practices for handling and analyzing large multi-scale datasets using examples drawn from open-data repositories.
Course Details
Repeats Allowed for Credit: 0
Crosslisted with: ES 288
Laboratory included
Normal Letter Grade only
Requisites and Restrictions
Instructor Permission Required: No
This is not an introduction to programming/scripting course. Students who are unsure of their preparation level should contact the instructor prior to enrollment to clarify the presumed statistical/computational/mathematical skillset.
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