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4502. Big Data Analytics

3.00 credits

Prerequisites: CSE 3500; MATH 2210Q; open only to students in the School of Engineering and declared Computer Science and Analytics minors.

Grading Basis: Graded

Focuses on basic concepts of data science and big data 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: 27-JAN-23 AM
To view current class enrollment click the refresh icon next to the enrollment numbers.
Term Campus Instruction Mode Instructor Section Session Schedule Location Enrollment Notes
Fall 2022 Storrs In Person Rajasekaran, Sanguthevar 001 Reg TuTh 3:30pm‑4:45pm
MCHU 201 30/40
Spring 2023 Storrs In Person He, Suining 001 Reg MoWeFr 10:10am‑11:00am
OAK 112 59/60
Spring 2023 Stamford In Person Bhargava, Kriti Z81 Reg MoWe 2:05pm‑3:20pm
DWTN 129 6/20