Graduate Course Descriptions

The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.

5255. Introduction to Data Science

3.00 credits

Prerequisites: Open to graduate students in Statistics, others with permission. Not open for credit to students who have passed STAT 3255. Recommended preparation: STAT 1000Q or 1100Q or 5005 or equivalent; STAT 2255 or equivalent; and STAT 3115Q or equivalent.

Grading Basis: Graded

Introduction to data science for effectively storing, processing, visualizing, analyzing and making inferences from data to enable decision making. Topics include project management, data preparation, data visualization, statistical modeling, machine learning, distributed computing and ethics.

Last Refreshed: 20-JUN-24 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2024 5890 Storrs In Person Yan, Jun
Mcclurg-Wong, Taelor
001 Reg MoWe 2:30pm‑3:45pm
1/5 AUST 313 3.00 Graded
Fall 2024 8788 Storrs In Person Yan, Jun 001 Reg MoWe 1:25pm‑2:40pm
2/5 AUST 445 3.00 Graded