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.

5405. Applied Statistics for Data Science

3.00 credits

Prerequisites: Instructor consent and undergraduate course in statistics. Not open to students who have passed STAT 5505 or STAT 5605 or BIST 5505 or BIST 5605.

Grading Basis: Graded

Statistics essential for data science incorporating descriptive statistics; integrative numerical description and visualization of data; graphical methods for determining and comparing distributions of data; data-driven statistical inference of one-sample, two-sample, and k-sample problems; linear regression; non-linear regression; and dependent data models.


Last Refreshed: 20-JUN-24 05.20.07.060883 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2024 5883 Storrs Online Asynchronous Ravishanker, Nalini
Guo, Jieyu
Zhong, Kelin
001 Reg 16/40 No Room Required - Online 3.00 Graded
Fall 2024 8783 Storrs In Person Ravishanker, Nalini 001 Reg TuTh 11:00am‑12:15pm
16/45 AUST 313 3.00 Graded