Instructors Zainab Riaz Room No. TBA Office Hours TBA Email zainab.riaz@lums.edu.pk Telephone 5130 Secretary/TA Hassan Haider/ TBA TA Office Hours Course URL (if any) COURSE BASICS Credit Hours 3 Lahore University of Management Sciences DISC 420 Business Analytics Spring Semester 2017 TBA suraj.lums.edu.pk/~ro/ Lecture(s) Nbr of Lec(s) Per Week 2 Duration 75 min. each COURSE DISTRIBUTION Core Elective Open for Student Category Close for Student Category MGS (Seniors), Open for MGS Seniors in phase II COURSE DESCRIPTION
Lahore University of Management Sciences Throughout the Management Science degree, students have already been exposed to a number of statistical and analytical techniques such as decision analysis, regression, optimization, etc. However, before these can be practically deployed as business analytics or business intelligence (i.e. analytical tools and techniques that rely on a business data to solve business problems) three things remain. These three things, described as follows, are the focus of this course: 1. Understanding the systems and their organization to support business analytics: Why use a data warehouse on top of a regular database? What components and processes have todays organizations developed to practically deal with capture and dissemination of business intelligence? (The top left circle in the diagram above) 2. Data mining techniques: Certain analytical techniques rely on computerized machine learning. There are supervised versus unsupervised learning and classification versus association. This course will approach the subject from a business perspective: what is the objective of the technique? What business problems can it resolve? And mostly hands on application of the tools. (the overlap between information systems circle and statistics circle) 3. Integration of business analytics topics: No real world problem comes with a label such as Use regression or the a priori algorithm. After studying data mining techniques, the student should now have a full menu of approaches at their disposal. It is the intelligence of the business professional that guides them in choosing which technique to employ. Hence, this course aims to help students practice this judgment call at a basic level in various situations essentially using a case based approach. COURSE PREREQUISITE(S) DISC 321 DISC 322 Decision Analysis Or equivalent & Optimization Methods in Management Science COURSE LEARNING OBJECTIVES 1. 2. 3. 4. Be aware of typical business intelligence systems components, processes and organizational architecture Learn about supervised and unsupervised data mining at a general level (not about details of the algorithms used but their benefits and limits, their required inputs and expected outputs and how to evaluate their performance). Be familiar with the vocabulary of data mining techniques, e.g. in text mining what is a corpus. Learn how to prioritize and choose between the analytical techniques across the degree. LEARNING OUTCOMES Upon completion of the course, students will be able to 1. Understand the typical vocabulary used across various business intelligence systems, especially with respect to datamining. 2. Avoid confusions created by proprietary names of business intelligence systems and components (e.g. SAP Business Objects, Teradata, Microstrategy, Zambeel, etc.) and understand their functionality regardless of organization they work for. 3. Select between descriptive, predictive and prescriptive analytical techniques according to the business problem at hand, or at least know who to refer to within their business intelligence organization in order to solve this problem. Efficiently self train to apply OR guide technical staff in applying data mining techniques when they engage 4. with the business intelligence systems and organization of modern businesses to solve typical real world business problems.
Lahore University of Management Sciences UNDERGRADUATE PROGRAM LEARNING GOALS & OBJECTIVES Goal 1 Effective Written and Oral Communication Objective: Students will demonstrate effective writing and oral communication skills Goal 2 Ethical Understanding and Reasoning Objective: Students will demonstrate that they are able to identify and address ethical issues in an organizational context. Goal 3 Analytical Thinking and Problem Solving Skills Objective: Students will demonstrate that they are able to identify key problems and generate viable solutions. Goal 4 Application of Information Technology Objective: Students will demonstrate that they are able to use current technologies in business and management context. Goal 5 Teamwork in Diverse and Multicultural Environments Objective: Students will demonstrate that they are able to work effectively in diverse environments. Goal 6 Understanding Organizational Ecosystems Objective: Students will demonstrate that they have an understanding of Economic, Political, Regulatory, Legal, Technological, and Social environment of organizations. Major Specific Learning Goals & Objectives Goal 7 (a) Discipline Specific Knowledge and Understanding Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations (Including subject knowledge). Goal 7 (b) Understanding the science behind the decision making process (for MGS Majors) Objective: Students will demonstrate ability to analyze a business problem, design and apply appropriate decision support tools, interpret results and make meaningful recommendations to support the decision maker Indicate below how the course learning objectives specifically relate to any program learning goals and objectives. PROGRAM LEARNING GOALS AND OBJECTIVES Goal 1 Effective Written and Oral Communication Goal 2 Ethical Understanding and Reasoning Goal 3 Analytical Thinking and Problem Solving Skills Goal 4 Application of Information Technology Goal 5 Teamwork in Diverse and Multicultural Environments Goal 6 Understanding Organizational Ecosystems Goal 7 (a) Discipline Specific Knowledge and Understanding Goal 7 (b) Understanding the science behind the decision making process COURSE LEARNING OBJECTIVES Objective #4 All objectives All objectives Objective #1 All objectives Objectives #2 & #4 COURSE ASSESSMENT ITEM Exam Lab Assignments, Exam Lab Assignments, Exam, Project Quizzes, Exam All items All items
GRADING BREAKUP AND POLICY Lahore University of Management Sciences Quizzes Assignments (In Class + Other) Mid Term Examination (on computer) Final Exam (Project Presentation) 20% 40% 20% 20% NOTE: PLEASE READ QUIZ POLICY BELOW EXAMINATION DETAIL Mid Term (In Class) Yes/No:... YES Combine/Separate:... Separate Duration:... 75 minutes (Tentatively) Exam Specifications:... Closed Book/Closed Notes; Lab based Exam... (Will need trading lab where the licensed software is... installed) Quizzes Petitions in general: Petitions should be submitted along with proper documentation (e.g. a medical certificate certifying illnesses or OSA certifying participation in OSA activity) and shall be approved on case by case basis. NOTE: OSA activities are planned events SO PLEASE BRING THESE (or at least e mail a scan) BEFORE THE CLASS YOU PLAN TO MISS. Later OSA petitions will be assumed not to be genuine. Quizzes: To keep the number of quizzes to a minimum, we reserve the right to use un announced quizzes. Quizzes will mostly be objective based in order to test understanding of vocabulary throughout the course or if they are tied to a case discussion, they may be subjective in that event. An n 1 policy will be applied only if the number of quizzes > 4. A missed (without petition approval) quiz will automatically be graded zero (0). You are required to attend with your designated section. Neither quiz nor assignments can be transferred to any other section, especially as the Learning Management System follows the Zambeel sectioned roll no. It is your responsibility to ensure that your details on LMS & Zambeel are up to date. COURSE OVERVIEW Sess. # 1. 2. TOPICS Introduction to Business Intelligence & Business Analytics Case: Managing with Analytics at P&G 1. How far into the levels of analytics (seen in last session) have Procter & Gamble progressed? 2. Should Torres over ride the forecast? What is the weakness in the BI if you say, Yes? 3. Data Visualization Basic 4. Data Visualization Exercise Case: King Kola Beverages Ltd. CC, Ch. 1 Case RECOMMENDED READINGS Managing with Analytics at P&G CC, Ch. 3 Albright et. al. Section on Pivot Tables SESSION OBJECTIVE(S) a. Build a mental map of the course b. Contract for the course a. How are the various levels of analytics employed in a modern organization b. What is the role of analysts & managers in the modern organization Generic concepts in Data Visualization Application of data visualization concepts
Lahore University of Management Sciences 5. 6. Advanced Data Visualization: the Grammar of Graphics (Introduction to R, R Studio & ggplot2 package) Tutorial Slides: Grolemund, Visualizing Data a. Advanced approaches to visualizing data b. Introduction to R and its graphing packages 7. Business Intelligence Architectures, Data 8. Warehousing & Big Data (Demonstration of Microstrategy & SAS) 9. 10. 11. 12. 13. Introduction to Cluster Analysis Cluster Analysis (in R) Introduction to Association Rules Association Rules (in R) Association & Clustering (Lab Assignment) V, Ch. 2 & Ch.3 CC, Ch. 6, Sec 6.3, p. 256 (review last) CC, Ch. 6, Sec 6.3, p. 265 (review last) Cluster Analysis: Hierarchical & K Means Cluster Analysis: Hierarchical & K Means in R Association Rules Mining A priori algorithm Association Rules Mining A priori algorithm in R Revision Assignment 14. MID TERM Data Mining > Supervised Learning Introduction to the process of supervised learning 15. Partitioning Data, Classification Accuracy, Prediction Accuracy 16. Data Mining > Supervised Learning k Nearest Neighbors (k NN) Classification and 17. Prediction 18. Data Mining > Supervised Learning 19. Classification Trees 20. Regression Trees 21. 22. 23. Data Mining > Supervised Learning Logistic Regression Data Mining > Supervised Learning Introduction to Neural Networks 24. Current trends in Data Mining and beyond Social Network Analysis 25. Text Mining 26. Comprehensive In Class Exercises 27. Comprehensive In Class Exercises 28. Review p. 269 p. 277 p. 283, 293 p. 299 PB, Ch 6 (Section 6.8) p. 158 To be distributed later Supervised learning: classification techniques Classification techniques: knn Classification techniques: trees Supervised Learning: Predictive modeling Learning Objectives # 1, 3 Introduction to Neural Networks All Learning Objectives Current trends in mining online data on social networking sites using network connections and text
TEXTBOOK(S)/SUPPLEMENTARY READINGS Lahore University of Management Sciences PLEASE OBTAIN THE COURSE PACK from the library for this course as there are multiple sources which have been used for the readings. Some of the textbook abbreviations used are explained below: [V] C. Vercellis (2009) Business Intelligence: Data Mining & Optimization for Decision Making Wiley. [CC] J. Camm, J. Cochran, M. Fry, et. al. (2015) Essentials of Business Analytics, Cengage. [PB] Stephen G. Powell and Kenneth R. Baker Management Science, The Art of Modeling with Spreadsheets, Wiley, 4 th Ed