1 / 19

ISM 250: Data Mining and Business Analytics Lecture 1

ISM 250: Data Mining and Business Analytics Lecture 1. Ram Akella TIM/UCSC akella@soe.ucsc.edu 650-279-3078. Course Structure. Business Analytics and context Data Mining Integration of two closely related topics via projects Theory, practice, industry/university experts

brendon-mawe
Télécharger la présentation

ISM 250: Data Mining and Business Analytics Lecture 1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ISM 250: Data Mining and Business AnalyticsLecture 1 Ram Akella TIM/UCSC akella@soe.ucsc.edu 650-279-3078

  2. Course Structure • Business Analytics and context • Data Mining • Integration of two closely related topics via projects • Theory, practice, industry/university experts • Lectures, lab, projects • Additional topic: Starting a company, and technology management

  3. Course Philosophy • Instructor provokes thought, stimulates, integrates • Students work ahead and after class, reading to prepare, work on labs and projects with Silicon Valley firms • Web has a great deal of information • Instructors role is to clarify, deepen understanding, help digest and integrate, and achieve new insights, problem solving and research

  4. Grading • (May alter to weight project/term/research paper more heavil, if of sufficiently high quality) • Weekly Homework on fundamental topics, quizzes/final, Comprehensive Course Project/term paper (including presentation to class) • Homework: 20% • Quizzes and final: 30% • Project/Term paper: 50% (Project Schedule is fast!) • Presentation: 10%

  5. Student Interest in Course • I have a BS in ..X.., MS in…Y.. • I would like to learn…. • I would like to be able do… • I would like to possibly do a startup……

  6. Student preparation: Name

  7. Course schedule, Modules and TAs • 1-2 weeks – One student takes care of everything • Labs by more experienced students

  8. Business Analytics

  9. Business Functions • Start -> Concept -> Product -> R&D-> Engineering • Verification/validation/promotion -> Marketing (includes pricing) • Selling -> Sales • Making (manufacturing)/delivering (services) -> Operations/Supply Chain (Customer-supplier networks for complex products) • Money to make it all work -> Finance? • IS/IT……….. • HR

  10. Issues • Learning customer preferences: Conjoint analysis • Demand-supply match of • Designers and products/projects • Orders and capacity • Uncertainty (queueing and delays) • => Constrained optimization

  11. Issues (continued) • Product portfolios to maximize profits • Given resources • Acquire resources • Goal: Speed to market (to achieve premium) • In finance and engineering • Marketing • Now, in E-Business: Web page layout optimization to maximize yield and revenue • Pricing • Product diffusion: Bass Model

  12. Issues (continued) • In product development, operations, finance • Options to acquire/buy/sell capacity, given uncertain demand • Tool kit: Stochastic Dynamic Programming (SDP) and Real Options (Decision Trees) • Use of SDP in Supply Chain Management • Use of SDP and constrained optimization in waterfall and spiral product development models • Integration with data mining

  13. Data Mining • Trends in demand • Changes • Anomalies • Quality characteristics: good/bad - classification • Price changes and clusters • Volume changes and clusters • Associations

  14. Text Mining and Search • Search for Product Component or Demand • Right match by • Description (text) • Price • Quality • Volume

  15. Data Mining (after next four slides)

  16. Technology Ventures??? • Discuss after DM slides and lecture is completed

  17. Next Class: Reading R1 • BA: Conjoint Analysis • Preferences in marketing • DM: Metrics and data in data mining • Products • Manufacturing • Knowledge

  18. Next Class : Assignment 1 • Read “The Search” and summarize 10 key ideas, rank ordered in descending priority • Bullet point format is OK

  19. Project • FitMe: Presentation today at 7 pm

More Related