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.

5717. Big Data Analytics

3.00 credits

Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500 and MATH 2210Q.

Grading Basis: Graded

Focuses on data science and big data analytics. Introduces basic concepts of data science and analytics. Different algorithmic techniques employed to process data will be discussed. Specific topics include: Parallel and out-of-core algorithms and data structures, Rules mining, Clustering algorithms, Text mining, String algorithms, Data reduction techniques, and Learning algorithms. Applications such as motif search, k-locus association, k-mer counting, error correction, sequence assembly, genotype-phenotype correlations, etc. will be investigated.


Last Refreshed: 18-AUG-22 05.20.21.840943 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2022 16057 Storrs Distance Learning He, Suining 001 Reg MoWe 4:40pm‑5:55pm
8/10 No Room Required - Online 3.00 Graded
Fall 2022 8406 Storrs In Person Rajasekaran, Sanguthevar 001 Reg TuTh 3:30pm‑4:45pm
19/20 MCHU 201 3.00 Graded
Fall 2022 14590 Storrs Online Wei, Wei 010X Reg 12:00am‑12:00am
17/20 No Room Required - Online 3.00 Graded