DISC 420 Business Analytics Fall Semester 2017 Instructors Zainab Riaz Room No. SDSB 4 38 Office Hours TBA Email zainab.riaz@lums.edu.pk Telephone 5130 Secretary/TA Sec: Muhammad Umer Manzoor, TA: TBA TA Office Hours TBA Course URL (if any) suraj.lums.edu.pk/~ro/ COURSE BASICS Credit Hours 3 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 This is a core course for MGS majors Elective for all other majors Freshmen (ACF majors only) COURSE DESCRIPTION
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 Decision Analysis (AND) DISC 322 Optimization Methods in Management Science COURSE LEARNING OBJECTIVES 1. Be aware of typical business intelligence systems components, processes and organizational architecture 2. 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). 3. Be familiar with the vocabulary of data mining techniques, e.g. in text mining what is a corpus. 4. 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. 4. Efficiently self train to apply OR guide technical staff in applying data mining techniques when they engage with the business intelligence systems and organization of modern businesses to solve typical real world business problems.
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 s, Exam Lab s, Exam, Project Quizzes, Exam All items All items
GRADING BREAKUP AND POLICY Attendance (4 allowed) Quizzes (5) s (In Class + Other) (8) Mid Term Examination (on computer) Final Exam Project 05% 15% 30% 15% 15% 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/Open Notes; Lab based Exam... (Will need trading lab where R Studio is... installed) Final Exam Yes/No:... YES Combine/Separate:... Separate Duration:... 120 minutes (Tentatively) Exam Specifications:... Closed Book/Open Notes; Lab based Exam... (Will need trading lab where R Studio is... installed) Policy on Quizzes and Attendance 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 > 5. A missed (without petition approval) quiz will automatically be graded zero (0). Attendance: Absents beyond 4 will be lead to 1 mark deduction per leave from overall marks. The only valid document for compensating for a leave is OSA approved application. No other applications will be entertained. Hard copy of OSA approved applications must be handed over to the instructor directly on the last course day. Electronic and email submissions will not be accepted. Compensation here means, average personal grade for that instrument would be applied in case of a valid OSA approved application submitted as a hard copy. COURSE OVERVIEW Sess. # TOPICS RECOMMENDED READINGS SESSION OBJECTIVE(S) 1. Introduction to Business Intelligence & Business Analytics a. Build a mental map of the course b. Contract for the course
2. Intro to R 3. Business Intelligence Architectures, Data Warehousing & Big Data 4. V, Ch.3 To get familiar with R and get required skillset for next sessions and advanced topics. Working directory, Script file, library and packages, objects in R, vectors, data frame, matrix etc. Learning Objective #1, 3 Learning Objective #1, 3 5. Getting to know your data JL, Ch. 2 Processing the information and understanding the data Visualization JL, Ch. 2 Generic concepts in Data Visualization 6. 7. 8. 9. 10. 11. 12. 13. Visualization Visualization Data Mining Intro to supervised and unsupervised learning Data Mining > Supervised Learning Introduction to the process of supervised learning Partitioning Data, Classification Accuracy, Prediction Accuracy Reading material will be JL, Ch. 8 Data Mining > Supervised Learning Simple Linear Regression JL, Ch. 3 Data Mining > Supervised Learning Classification Trees Regression Trees JL, Ch. 13 Base Plots GGPlot2 GGPlot2 Concepts and examples of what the two learning techniques are. Learning Objective #1, 3 Supervised learning: classification techniques Learning Objective #1, 3 Supervised learning: classification techniques Learning Objective #1, 3 Supervised learning: classification techniques Decision Trees 14. Data Mining > Supervised Learning 15. k Nearest Neighbors (k NN) JL, Ch. 9 16. MID TERM 17. Project Data Presentation Learning Objective #1, 3 Classification techniques: knn Submission of one page project synopsis (week 14) 18. Logistic Regression JL, Ch. 7 Supervised Learning: Predictive 19. modeling
20. Data Mining > Unsupervised Learning 21. > Introduction to Cluster Analysis 22. 23. Data Mining > Unsupervised Learning > Cluster Analysis 24. Data Mining > Unsupervised Learning 25. > Introduction to Association Rules JL, Ch. 15 JL, Ch. 15 JL, Ch. 16 26. Text Mining Reading: 27. Business Intelligence from User Generated Content Cluster Analysis: K Means Cluster Analysis: Hierarchical Association Rules Mining A priori algorithm Introduction to text mining and its applications 28. Network Analysis JL, Ch. 20 Introduction to Network Analysis TEXTBOOK(S)/SUPPLEMENTARY READINGS 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. [JL] J. Ledolter (2013) DATA MINING AND BUSINESS ANALYTICS WITH R Wiley.