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: 23-FEB-24 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2024 7339 Storrs In Person Wei, Wei 001 Reg TuTh 12:30pm‑1:45pm
27/20 MCHU 302 3.00 Graded
Spring 2024 13389 Storrs Online Asynchronous Wei, Wei 002 Reg 17/25 No Room Required - Online 3.00 Graded Reserved for MEng
Fall 2024 4846 Storrs In Person Rajasekaran, Sanguthevar 001 Reg TuTh 11:00am‑12:15pm
0/30 BOUS A106 3.00 Graded
Fall 2024 4847 Storrs In Person Wei, Wei 002 Reg TuTh 12:30pm‑1:45pm
0/20 MCHU 205 3.00 Graded