Data Mining for Business Analytics ISOM3360 (L3): Spring 2018

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1 Data Mining for Business Analytics ISOM3360 (L3): Spring 2018 Course Name Data Mining for Business Analytics Course Code ISOM 3360 (3 Credits) Exclusion COMP 4331 Prerequisite ISOM 2010 Instructor Yi Yang, ISOM Contact Office: LSK Begin subject: [ISOM3360] Office Hours Tuesday 4pm-5pm Friday 4pm-5pm Course Schedule and Classroom Lecture: Monday 13:30-14:50 (LSK 1010) Friday 09:00-10:20 (LSK 1010) Lab 1: Thur 10:30 11:20am (LSK G005) Lab 2: Thur 12:00 12:50pm (LSK G005) Lab 3: Thur 9:00 9:50am (LSK G005) Lab 4: Thur 3:00 3:50pm (LSK G005) Course Webpage Accessible from Canvas Teaching Assistant Sophie Gu (LSK 6031) 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. 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

2 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. 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. 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 All courses materials (Lecture slides, assignments, and lab handouts) are available on the class website. Supplemental books (optional): 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: Grading Your grades will be determined based on class and lab participation, homework assignments, the midterm and final exam, and group project. Lab Participation 7% Homework Assignments (2) 18%

3 Group Project 20% Midterm Exam 25% Final Exam 30% 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 Microsoft Azure. 5. Homework Assignment, Term Project and Exams Homework Assignment There will be a total of 2 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. Term project You are expected to finish a term project. The term project is a teamwork, which means you need to first form a team. Each team includes 3-4 students. In this project, you will apply the data mining techniques you learned in the class

4 to solve real-world problems. The deliverable is a written report summarizing what you have done and what you have achieved. More details will be provided later. Exams 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 20 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. Schedule of Lectures and Labs (subject to change) Week Date Topics Due 1 Feb 2 Course Introduction 2 Feb 5 Feb 9 Overview of Data mining process Data Preparation and data Visualization 3 Feb 12 Feb 16 Prediction: Decision Tree I [No Class] Lunar New Year break 4 Feb 19 Feb 23 [No Class] Lunar New Year break Prediction: Decision Tree II Team Formation 5 Feb 26 Model Selection and Evaluation Measures Mar 2 Prediction: Linear Regression 6 Mar 5 Prediction: Logistic Regression Project Idea

5 Mar 9 Project Idea Meeting 7 Mar 12 Prediction: Naïve Bayes Homework 1 Mar 16 Midterm Exam Review 8 Mar 19 Class Cancelled for Midterm Exam Mar 23 Text Mining 9 Mar 26 Mar 30 Feature Selection [No Class] Mid-Term break 10 April 2 April 6 [No Class] Mid-Term break Prediction: k-nearest neighbor 11 April 9 April 13 Application: Recommender System Project Progress Meeting 12 April 16 April 20 Relationship Mining: Association Rule Relationship Mining: k-means 13 April 23 Ensemble learning Homework 2 April 27 Neural Network and Deep Learning 14 April 30 Network Analytics May 4 Search Engine Technology Project final report 15 May 7 Final Exam Review

6 Lab Session Schedule Lab No. Date Topics 1 Feb. 8 Data visualization and Data Preprocessing 2 Feb. 15 Decision tree 3 Mar. 1 Decision tree II 4 Mar. 8 Cost-sensitive learning 5 Mar. 15 Linear Regression and Logistic Regression 6 Mar. 22 Naïve Bayes 7 Mar. 29 Text Mining & Sentiment Analysis Apr. 5 Cancelled for Midterm Week 8 Apr. 12 Association Rule & Clustering 9 Apr. 19 KNN 10 Apr. 26 Ensemble learning 11 May. 3 Collaborative Filtering

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