Office: LSK 5045 Begin subject: [ISOM3360]...

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1 Business Intelligence and Data Mining ISOM 3360: Spring 2015 Instructor Contact Office Hours Course Schedule and Classroom Course Webpage Jia Jia, ISOM Office: LSK 5045 Begin subject: [ISOM3360]... <--- Note! Mon 13:00-15:00 and by appt. Lecture: Mon 15:00 16:30 Fri 10:30-12:00 ( LSK1001) Lab: Mon 13:30-14:30 (LSKG005) Accessible from LMES 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 decisionmaking 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, market segmentation, credit risk management, e-commerce, financial trading, online recommendation, 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. In order to accommodate the emerging need for big data applications, modern distributed file systems and MapReduce algorithms and some other large-scale algorithms will also be covered. The course is designed for students with various backgrounds, while some basic mathematical knowledge of linear algebra, calculus and probability is a prerequisite. After taking this course you should: 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.

2 3. Be able to identify the right BI tools/techniques for various business problems. Gain hands-on 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 posted to LMES or distributed in class. Supplemental book (optional): Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, third Edition, by Michael Berry and Gordon Linoff, Wiley, 2011 ISBN: Towards Data Science: Fundamental principles of data mining and data-analytic thinking, Draft, by Foster Provost, Tom Fawcett Many students find this book to be an excellent supplemental resource. In the class schedule below I suggest the most important sections to read to supplement each class module. 3. Requirements and Grading The grade breakdown is as follows: 1. Lab participation: 10% 2. Homework (4): 40% 3. Midterm quiz: 20% 4. Final exam: 30% 4. Important Notes on the Lab Session This is primarily a lecture-based course, but student 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 top data mining tools e.g.,weka and R. 5. Homework Assignment and Exams There will be a total of 4 individual homework, each comprising questions to be answered and hands-on tasks. Completed assignments must be handed in prior to the start of the class on the

3 due date. If submitted by they must arrive at least one hour prior to the start of class. Assignments will be graded and returned promptly. Assignments are due prior to the start of the lecture on the due date. 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. The in-class mid-term quiz is to be tentatively scheduled on March, 30. 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. The quiz and exam must be taken at their scheduled times; make up quizzes and exams will only be given for special cases, in accordance with University guidelines. Tentative Schedule of Lecture Topics The following table shows the planned list of topics that we will cover in each class as well as the assignment due dates. Please note that this schedule is tentative and may be adjusted as the semester progresses. Class Number Date Topics Assignment Dates 1 Feb. 2 What is BI? Why BI now? What is data mining? DM process. DM tasks 2 Feb. 6 3 Feb. 9 Decision tree learning. Business application: Customer segmentation 4 Feb. 13 Model evaluation. Cost-sensitive learning. ROC graph 6 Feb. 23 Linear and logistic regression Business application: Customer retention 7 Feb Mar. 2 Model selection Homework 1 Mar. 6 Mar. 9 "naïve" Bayes and linear discriminant analysis Business application: spam filtering and financial news trading

4 11 Mar Mar. 16 Support vector machines Mar. 20 Unsupervised learning: association rule learning and Mar. 23 clustering analysis Business application: customer Segmentation Homework 2 Mar. 27 Midterm review Mar. 30 Midterm quiz (Coverage 1-12) 17 Apr. 10 Large-scale data processing: MapReduce framework Apr. 13 Recommender system in e-commerce Homework 3 Apr Apr. 20 Apr.24 Apr. 27 May 4 Search engine (SE) analytics: How does SE work? What is SE marketing? How to combat web spam? Advertising on the web Social Networks 24 May 8 Final Exam Review Homework 4

5 Lab Session Schedule Lab Date Topics Number 1 Feb. 9 Weka installation, Weka demo (data type, format, loading) 2 Feb. 16 R installation and data exploration with R 3 Feb. 23 Decision tree learning with Weka 4 Mar. 2 Decision tree vs. logistic regression with Weka 5 Mar. 9 Model selection with R 6 Mar. 16 Financial news trading (naive bayes) with Weka 7 Mar. 23 SVM with R 8 Apr. 13 Association rule learning with Weka 9 Apr. 20 MapReduce application 10 Apr. 27 Target marketing application 11 May. 4 Social network analysis application (tentative)