Data Mining for Business Analytics. ISOM 3360 (L1): Spring 2017

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1 Data Mining for Business Analytics ISOM 3360 (L1): Spring 2017 Course Name Data Mining for Business Analytics Course Code ISOM 3360 No. of Credit 3 Credits Exclusion(s) COMP 4331 Prerequisite(s) ISOM 2010 Professor Jing Wang, ISOM Contact Office: LSK 4044 Tel: jwang@ust.hk Begin subject: [ISOM3360] Office Hours By appointment Course Schedule and Classroom Lecture: Tue, Thur 3:00 4:20pm (LSK 1034) Lab 1: Thur 4:30 5:20pm (LSK G005) Lab 2: Fri 3:00 3:50pm (LSK G005) Lab 3: Fri 10:30 11:20am (LSK G005) Course Webpage Accessible from Canvas Teaching Assistant Sophie Gu (LSK 6031) Tel: imsophie@ust.hk TA Office Hours By appointment 1. Course Overview This course will change the way you think about data and its role in business. Businesses, governments, and individuals create massive collections of data as a byproduct of their activity. Increasingly, decision-makers rely on intelligent technology to analyze data systematically to improve decision-making. In many cases, automating analytical and decision-making processes is necessary because of the volume of data and the speed with which new data are generated. In virtually every industry, data mining has been widely used across various business units such as marketing, finance and management to improve decision making. In this course, we discuss specific scenarios, including the use of data mining to support decisions in customer relationship management (CRM), market segmentation, credit risk management, e-commerce, financial trading and search engine strategies. The course will explain with real-world examples the uses and some technical details of various data mining techniques. The emphasis primarily is on understanding the business application of data mining techniques, and secondarily on the variety of techniques. We will discuss the mechanics of how the methods work only if it is necessary to understand the general concepts and business applications. You will establish analytical thinking to the problems and understand that proper application of technology is as much an art as it is a science. The course is designed for students with various backgrounds -- the class does not require any technical skills or prior knowledge. After taking this course you should:

2 1. Approach business problems data-analytically (intelligently). Think carefully & systematically about whether & how data can improve business performance. 2. Be able to interact competently on the topic of data mining for business intelligence. Know the basics of data mining processes, techniques, & systems well enough to interact with business analysts, marketers, and managers. Be able to envision data-mining opportunities. 3. Be able to identify the right BI tools/techniques for various business problems. Gain handson experience in using popular BI tools and get ready for the job positions that require familiarities with the BI tools. 2. Lecture Notes and Readings Lecture notes For most classes I will hand out lecture notes, which will outline the primary material for the class. Other readings are intended to supplement the material we learn in class. They give alternative perspectives and additional details about the topics we cover: Supplemental readings Supplemental readings posted to Canvas or distributed in class. Supplemental books (optional): Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, third Edition, by Michael Berry and Gordon Linoff, Wiley, 2011 ISBN: Data Science for Business: What you need to know about data mining and data-analytic thinking, by Foster Provost, Tom Fawcett, O'Reilly Media, 2013 ISBN: Requirements and Grading Your grades will be determined based on class and lab participation, homework assignments, the midterm exam, and the final exam. Component Percentage Class Participation 3% Lab Participation 7% Homework Assignments (3) 30% Midterm Exam 30% Final Exam 30%

3 4. Important Notes on the Lab Session This is primarily a lecture-based course, but lab participation is an essential part of the learning process in the form of active practice. You are NOT going to learn without practicing the data analysis yourselves. During the lab session, I will expect you to be entirely devoted to the class by following the instructions. And you should actively link the empirical results you obtained during the lab to the concepts you learned in the lectures. During the Lab session, you will gain hands-on experience with the (award-winning) toolkit Weka ( and a very popular online BI service from Microsoft. 5. Homework Assignment and Exams There will be a total of 3 individual homework assignments, each comprising questions to be answered and hands-on tasks. Completed assignments must be handed in via Canvas prior to the start of the class on the due date. Assignments will be graded and returned promptly. Turn in your assignment early if there is any uncertainty about your ability to turn it in on the due date. Assignments up to 24 hours late will have their grade reduced by 25%; assignments up to one week late will have their grade reduced by 50%. After one week, late assignments will receive no credit. This course will have two closed-book exams. The midterm exam will test issues covered in the first half of the course. The final exam will cover the classes in the second half of the course. Review sessions will be scheduled to help you prepare for these examinations. The midterm exam is tentatively scheduled on March 21 st (7:00-9:00pm). Let me know as early as possible if there is any unavoidable conflict. The final exam will be held during the final examination period; the date will be announced later in the semester.

4 Tentative Schedule of Lectures and Labs This schedule is tentative and may be adjusted as the semester progresses. Week Date Topics Due 1 Feb 2 Overview of the Course Feb 7 Feb 9 Feb 14 Feb 16 Feb 21 Feb 23 Data Mining and Relation to Other Data Analytic Techniques Data Mining Basics Decision Tree Learning Business Application: Predicting Customer Default Overfitting and Model Selection More on Evaluation: Cost-Sensitive Learning 5 Feb 28 Linear Regression Homework 1 Due Mar 2 Logistic Regression 6 Mar 7 Naïve Bayes Classifier Mar 9 Business Application: Financial News Trading 7 Mar 14 Association Rule Learning Business Application: Basket Analysis Mar 16 Midterm Review 8 Mar 21 [No Class] Midterm Exam (7:00-9:00pm) Mar 23 9 Mar 28 Clustering Methods Business Application: Customer Segmentation Mar 30 Nearest Neighbor Classification Homework 2 Due 10 April 4 Ching Ming Festival (No Class) April 6 Business Application: Recommender Systems in E-Commerce 11 April 11 Ensemble Learning April 13 Mid-Term Break (No Class) 12 April 18 Mid-Term Break (No Class) April 20 Search Engine Technology 13 April 25 Search Engine Marketing April 27 Social Network Analysis 14 May 2 TBD [for Synchronization] May 4 Neural Networks and Deep Learning Homework 3 Due 15 May 9 Final Exam Review

5 Lab Session Schedule Number Date Topics 1 Feb. 9&10 Data visualization (Excel) 2 Feb. 16&17 Weka introduction and Decision tree (Weka) 3 Feb. 23&24 Microsoft Azure introduction & Decision tree (Azure) 4 Mar. 2&3 Cost-sensitive learning 5 Mar. 9&10 Linear Regression and Logistic Regression (Weka & Azure) 6 Mar. 16&17 Naïve Bayes (Weka) Mar. 23&24 Cancelled for Midterm Week 7 Mar. 30&31 Text Mining (Weka) 8 Apr. 6&7 Sentiment Analysis (Azure) 9 Apr. 20&21 Association Rule (Weka) & Clustering (Weka) 10 Apr. 27&28 KNN (Weka) & Collaborative Filtering (Azure) 11 May 4&5 TBC

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