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 • Lectures, lab, projects • Additional topic: Starting a company, and technology management
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
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%
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……
Student preparation: Name Strong Linear Algebra: Bases & transforms Orthogonalization SVD Statistics Hypothesis testing Regression ANOVA Stochastics Markov Chains Queueing Weak
Course schedule, Modules and TAs • 1 -2 weeks – One student takes care of everything • Labs by more experienced students
Business Analytics
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
Issues • Learning customer preferences: Conjoint analysis • Demand-supply match of – Designers and products/projects – Orders and capacity – Uncertainty (queueing and delays) • => Constrained optimization
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
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
Data Mining • • Trends in demand Changes Anomalies Quality characteristics: good/bad - classification Price changes and clusters Volume changes and clusters Associations
Text Mining and Search • Search for Product Component or Demand – Right match by • • Description (text) Price Quality Volume
Data Mining (after next four slides)
Technology Ventures? ? ? • Discuss after DM slides and lecture is completed
Next Class: Reading R 1 • BA: Conjoint Analysis – Preferences in marketing • DM: Metrics and data in data mining • Products – Manufacturing – Knowledge
Next Class : Assignment 1 • Read “The Search” and summarize 10 key ideas, rank ordered in descending priority • Bullet point format is OK
Project • Fit. Me: Presentation today at 7 pm
Скачать презентацию ISM 250 Data Mining and Business Analytics Lecture
148a426e06a0a8fa519da2412e5c1d01.ppt