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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: 29-MAR-24 05.20.11.748914 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
Spring 2024 Storrs In Person Wei, Wei 001 Reg TuTh 12:30pm‑1:45pm
MCHU 302 43/50
Spring 2024 Stamford In Person Sherafat, Elham 881 Reg TuTh 3:30pm‑4:45pm
DWTN 233 11/30
Fall 2024 Storrs In Person Rajasekaran, Sanguthevar 001 Reg TuTh 11:00am‑12:15pm
PWE 175 43/50
Fall 2024 Storrs In Person Wei, Wei 002 Reg TuTh 12:30pm‑1:45pm
MCHU 205 41/50