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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: 06-MAY-21 05.20.19.123041 AM
|1218 9699 1 001||Fall 2021||Storrs||Distance Learning||Rajasekaran, Sanguthevar||001||Reg||TuTh 3:30pm‑4:30pm
||No Room Required - Online||36/40|