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4502. Big Data Analytics
3.00 credits
Prerequisites: CSE 3500; MATH 2210; 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: 20-SEP-19 05.20.20.002631 AM
| Term | Campus | Instruction Mode | Instructor | Section | Session | Schedule | Location | Enrollment | Notes | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1198 10606 1 001 | Fall 2019 | Storrs | In Person | Rajasekaran, Sanguthevar | 001 | Reg | TuTh 2:00pm‑3:15pm |
UTEB 175 | 32/40 | Combined with CSE 5717-001 |
| 1203 9928 1 001 | Spring 2020 | Storrs | In Person | Wei, Wei | 001 | Reg | TuTh 11:00am‑12:15pm |
0/40 |