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5717. Big Data Analytics
3.00 credits
Prerequisites: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500 and MATH 2210Q.
Grading Basis: Graded
Focuses on data science and big data analytics. Introduces basic concepts of data science and 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.
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