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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.