Big Data Analytics Adv. Cert. - Curriculum
Big Data Analytics, advanced certificate, typical course sequence
Introduction to Big Data
This course provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. First, practical techniques used in exploratory data analysis and mining are introduced; topics include data preparation, visualization, statistics for understanding data, and grouping and prediction techniques. Second, approaches used to store, retrieve, and manage data in the real world are presented; topics include traditional database systems, query languages, and data integrity and quality. Case studies will examine issues in data capture, organization, storage, retrieval, visualization, and analysis in diverse settings such as urban crime, drug research, census data, social networking, and space exploration. Big data exploration and management projects, a term paper and a presentation are required. Sufficient background in database systems and statistics is recommended. (Prerequisite: CSCI-603 or CSCI-605 with a grade of B or better or (CSCI-320 or SWEN-344). May not take and receive credit for CSCI-620 and CSCI-420. If earned credit for/or currently enrolled in CSCI-420 you will not be permitted to enroll in CSCI-620.) Lecture 3 (Fall, Spring, Summer).
Big Data Analytics
This course provides a graduate-level introduction to the concepts and techniques used in data mining. Topics include the knowledge discovery process; prototype development and building data mining models; current issues and application domains for data mining; and legal and ethical issues involved in collecting and mining data. Both algorithmic and application issues are emphasized to permit students to gain the knowledge needed to conduct research in data mining and apply data mining techniques in practical applications. Data mining projects, a term paper, and presentations are required. (Prerequisites: CSCI-620 or (CSCI-420 and CSCI-320) or (4003-485 and 4003-487) or equivalent course.) Lecture 3 (Fall, Spring).
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