Lecture: Mon 13:30 14:50 Fri 9:00-10:20 ( LTH, Lift 27-28) Lab: Fri 12:00-12:50 (Rm. 4116)

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1 Business Intelligence and Data Mining ISOM 3360: Spring 203 Instructor Contact Office Hours Course Schedule and Classroom Course Webpage Jia Jia, ISOM Office: Rm 336 (Lift 3-) Begin subject: [ISOM3360]... <--- Note! Fri :00 PM - :00 PM and by appt. Lecture: Mon 3:30 :50 Fri 9:00-0:20 ( LTH, Lift 27-2) Lab: Fri 2:00-2:50 (Rm. 6) Accessible from LMES. 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 (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:. 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, 20 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:. Lab participation: 0% 2. Homework (3): 30% 3. Midterm quiz: 30%. Final exam: 30%. 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 the (award-winning) toolkit Weka.( and a very popular BI software from SAS. 5. Homework Assignment and Exams There will be a total of 3 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 2 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 mid-term quiz is to be tentatively scheduled on March,. 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 and Readings The following table shows the planned list of topics that we will cover in each class as well as the assignment due dates. Please take note that this schedule is tentative and may be adjusted as the semester progresses. Class Number Date 25 Topics What is BI? Why BI now? What is data mining? DM process. DM tasks. Data visualization DM basics. Decision tree learning. Business application: Customer Segmentation Model evaluation. Cost-sensitive learning. Logistic regression Business application: Customer retention Assignment Dates Homework

4 Midterm Review Midterm Quiz "naïve" Bayes and text classification Business application: spam filtering and financial news trading 25 5 Descriptive data May 3 2 May 6 25 May 0 mining, unsupervised algorithms, association rule learning Clustering analysis Business application: Customer Segmentation Nearest neighbor prediction. Recommender system in electronic commerce. Search engine (SE) analytics: How does SE work? What is SE marketing? Web analytics Final Exam Review Homework 2 Homework 3

5 Lab Session Schedule Lab Date Topics Number Excel data visualization 2 SAS data visualization 3 Weka installation, Weka demo (data type, format, loading) Decision tree I 5 Decision tree II, SAS 6 Weka more 7 5 Financial news trading (variable selection, naive bayes) 2 Association rule 9 9 Target marketing application 0 26 TBA