1. ISM 250: Data Mining and Business Analytics Lecture 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 StrongWeak Linear Algebra: Bases & transforms Orthogonalization SVD Statistics Hypothesis testing Regression ANOVA Stochastics Markov Chains Queueing

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