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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: 22-APR-24 05.20.14.479040 AM
Term | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Location | Enrollment | Notes | |
---|---|---|---|---|---|---|---|---|---|---|
1243 7374 1 001 | Spring 2024 | Storrs | In Person | Wei, Wei | 001 | Reg | TuTh 12:30pm‑1:45pm |
MCHU 302 | 43/50 | |
1243 13119 1 881 | Spring 2024 | Stamford | In Person | Sherafat, Elham | 881 | Reg | TuTh 3:30pm‑4:45pm |
DWTN 233 | 11/30 | |
1248 4887 1 001 | Fall 2024 | Storrs | In Person | Rajasekaran, Sanguthevar | 001 | Reg | TuTh 11:00am‑12:15pm |
PWE 175 | 46/50 | |
1248 4888 1 002 | Fall 2024 | Storrs | In Person | Wei, Wei | 002 | Reg | TuTh 12:30pm‑1:45pm |
MCHU 205 | 38/50 |