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BUSINESS ANALYTICS Sub Code: 010020 Course Credits: 03 Course Snapshot Large volumes of data have been collected by organizations using enterprise applications like ERP, SCM and CRM. Most of the data is being analyzed for operational purposes. Very few are using the information for Strategic Decision Making. Business Intelligence (or BI) objective is to derive information from large volume of data and apply this for strategic decision making to gain a competitive edge. The course will expose the students to tools and techniques used in BI applications. It will also help the students to appear for SAS Enterprise Certification Program conducted globally. Objectives Upon completion of the course it is expected that students will be able to understand: Relevance of Business Intelligence in Industry Role of Business Intelligence in decision making Technology to implement Business Intelligence Importance of Data Warehouse to implement Business Intelligence Data Mining and Data Visualization Future Directions Learning Goals a. General Learning Goals Effective Communication Interpersonal Skills & Teamwork Social Responsibility Problem Solving & Decision Making Quantitative Analytical Skills Global Awareness (L 1) (L 2) (L 3) (L 4) (L 5) (L 6) b. Specific Learning Goals Knowledge: Understand the process for linking data to critical business outcomes (K1) Deployment of Business analytics to arrive at effective and efficient decisions. (K2) To recognize strengths and identify any needs for improvement in various business functions. (K3) Skills: How to calculate ROI on various investments Data Visualization (S1) (S2) Attitude: Explorative Mind set Objectivity Holistic view (A1) (A2) (A3) Course Delivery Classroom Engagement (CE)- contact hours in the class 30 Hours (lectures, case discussions, presentations, exercises, internal assessment) Directed Learning (DL)- done under faculty supervision 30 Hours (lab-work, research, fieldwork, project work) Total No. of Hours (CE+DL) = 60 Evaluation Components Internal Assessment 60% Cases: Term Oral and Written Presentation Case Discussions Participation: Including responses to cases/topics Project Mid-Term Exam- 2 hours External Assessment 40% End-Term Exam- 3 hours 15% 10% 15% 20% 40% Study Material Text Book: Data Warehousing Fundamentals – Paulraj Ponniah − Reference Book Data Mining Techniques – A Berry and Gordon Linoff Reference Book: Cerrito, P. B. 2006. Introduction to Data Mining Using SAS Enterprise Miner, Cary, NC: SAS Institute Inc. de Ville Barry, 2006. Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc. Biggs, D. B. de Ville, and E. Suen. 1991. “A Method of Choosing Multiway Partitions for Classification and Decision Trees.” Journal of Applied Statistics 18, no. 1 : 49-62. Breiman, L. 1996. “Bagging Predictors,” Machine Learning 24, No. 2 : 123-140. Breiman, L. 2001. “Random Forests,” Machine Learning 45, No. 1 : 5-32 Breiman, L. 2002. “WALD Lectures.” Paper presented at the sixty-fifth annual meeting of the Institute of Mathamatical Statistics, Banff, Alberta, Canada. Available at http://www.stat.barkeley.edu/users/breiman/. Breiman, L., et al. 1984. Classification and Regression Trees, Belmont, CA: Wadsworth. Michie, D., C.C. Spiegelhalter, and C. C. Taylor, eds. 1994. Machine Learning, Neural and Statistical Classification, New York, NY: Ellis Horwood. Pyle, Dorian, 1999, “Data Preparation for Data Mining. San Francisco, CA: Morgan Kaufmann Publishers Inc. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning, San Francisco, CA: Morgan Kaufmann Publishers Inc. Data Warehousing Institute Website Course Material on LMS Course Outline Articles, Cases and Reference material Session Plan-Classroom Engagement (CE) - each session is for 90 min Session Topic No 1-2 3-4 4-5 5-11 Introduction to Business Intelligence (BI), Components of Business Intelligence Overview and Concepts of Data Warehouse Planning a Data warehouse Defining Business Requirements, Role of Metadata Star Schema and Multi Dimension Database, OLAP Lab Sessions on Building a Data warehouse Teaching Resource Pedagogy Assessment Text Book PPT and classroom discussion Group Discussion, Presentation Text Book & reference material Case presentation, analysis & discussion Group Case Discussion Presentation & Group CaseAnalysis Presentations Text Book & reference material PPT & discussion Group Discussion Text Book & reference material Lab Sessions K-S-A K1-K3 S1-S2 A1-A3 Learning Goals L4,L5 K1-K3 S1-S2 L4,L5 A1-A3 K1-K3 S1-S2 A1-A3 Lab Practical and hand K1-K3 on experience on S/W S1-S2 A1-A3 L4,L5 L4,L5 12 Introduction to Data Mining Text Book & reference material 13 Data Mining Methodology Text Book & reference material 14 Data Transformation, identifying variable of importance, Data Sampling, Data Exploration Text Book & reference material 15 Decision Trees Logistic Regression Disc. Analysis Text Book & reference material 16-20 Lab Sessions using SPSS Text Book & reference material Case presentation, analysis & discussion Group Case Discussion Presentation & Group Case Analysis Presentations K1-K3 S1-S2 A1-A3 L4,L5 Case Group Case presentation, Discussion analysis & Presentation discussion K1-K3 S1-S2 A1-A3 L4,L5 Case Group Case presentation, Discussion analysis & Presentation discussion K1-K3 S1-S2 A1-A3 L4,L5 Case presentation, Group Discussion, analysis & Lab practical discussion K1-K3 S1-S2 A1-A3 L4,L5 Lab Sessions K1-K3 S1-S2 A1-A3 L4,L5 Lab Practical and problem solving Total Hours: 30 Work Plan- Directed Learning (DL) Sl. No. Description of Component (Lab-work, research, fieldwork, project work, etc.) No. of Hours required 20 2 Output/Deliverable from students Presentation Presentation 1 2 Case Business Analytics lab 3 Assignment-1 2 Assignment 4 5 Assignment-2 Project 2 4 Total Hours- 30 Assignment Short report