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: Prerequisites: Open to graduate students in the CSE program, others with permission. Recommended preparation: CSE3500 and MATH2210. (RG5910)

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: 30-OCT-20 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Fall 2020 11083 Storrs Distance Learning Rajasekaran, Sanguthevar 001 Reg TuTh 2:00pm‑3:15pm
Waitlist Spaces: 30
No Room Required - Online 3.00 Graded Combined with CSE 4502-001
Spring 2021 16083 Storrs Distance Learning He, Suining 001 Reg MoWeFr 10:10am‑11:00am
4/10 No Room Required - Online 3.00 Graded