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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: 25-NOV-22 05.20.18.718397 AM
| Term | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Location | Enrollment | Notes | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1228 7078 1 001 | Fall 2022 | Storrs | In Person | Rajasekaran, Sanguthevar | 001 | Reg | TuTh 3:30pm‑4:45pm |
MCHU 201 | 30/40 | |
| 1233 6425 1 001 | Spring 2023 | Storrs | In Person | He, Suining | 001 | Reg | MoWeFr 10:10am‑11:00am |
OAK 112 | 49/60 | |
| 1233 15573 1 Z81 | Spring 2023 | Stamford | In Person | Bhargava, Kriti | Z81 | Reg | MoWe 2:05pm‑3:20pm |
5/20 |