Knowledge Services and Data Analytics |
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Location: UCSC Silicon Valley Center at NASA (Mountain View): Room 2069 UCSC Main campus: Room , Baskin Engineering Room 156
Time: 6pm-9:30pm Tuesday, Thursday Classes on Tuesdays, TA sessions on Thursdays
Instructors: Ram Akella (akella (at) soe.ucsc.edu)
TA: Jyotsna Gangwar (jyotsna (at) soe.ucsc.edu)
SVC Instructional support: Vanessa Binder, vbinder(at)soe.ucsc.edu, 650-528-4030 x146)
WebCT (for homework submission)
XLMiner at Santa Cruz: GradPC2 and GradPC4 in BE316
XLMiner and Matlab at SVC: tim-lab11 and tim-lab12 located in the rooms 2072 and 2071 respectively —————————————————————————————————-
Course Description: ISM209 is part of the following ISTM/TIM streams: - Information Retrieval and Knowledge Management, with 245 (Data Mining) and 260(IRKM) and - Management of Technology and Services, with 206 (Optimization) and 207 (Stochastic models) You will learn some of the following skills: Learn why leading firms (and startups) such as as Google, Yahoo, Microsoft, IBM, HP, Fair Isaacs, and Cisco, are focusing on data and business analytics in providing “knowledge management analytics and services” for complex enterprises analyze and synthesize business intelligence platform needs at the algorithmic decision making level, including functions such as marketing and sales through data and text mining, new product development speedup, supply chain and e-business optimization, (and possibly financial engineering and risk management). learn basic analytics of data mining, including statistics, supervised and unsupervised learning approaches, search engine and modern information system retrieval analyze intelligent support systems for marketing decisions, including fundamental methods such as conjoint analysis, together with web search, information retrieval, and data mining approaches, for learning about markets and customer preferences, as well as develop mathematical models for optimizing sales, marketing, and pricing decisions in high tech learn basic of constrained optimization and dynamic programming, including value and policy iteration for finite horizon situations, with applications in supply chain management and e-business, as well as web recommender systems perform financial and decision analysis to manage risk and to develop technologies and products that are profitable, by learning financial engineering ideas in real options and portfolios based on constrained optimization ——————————————————————————————————
Grading: Grades will be based on:
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