Carl H. Lindner College of Business University of Cincinnati

Transcription

1 Carl H. Lindner College of usiness University of Cincinnati 22-IS-7036 dvanced usiness Intelligence Spring 2014 February 24 pril 19, 2014 Section 1: Tuesday and Thursday: 4:00 5:50pm, RECCENTR 3240 Section 2: Monday: 6:00 9:30pm, LINDNER 220 Instructor: Zhe (Jay) Shan Office: 509 Lindner Hall Phone: (Please include IS7034 in the subject line of your s. is the best way to contact me. I should reply almost all your s within 24 hours.) Office Hours: Tuesday and Thursday (3 4pm), and by appointment Course Overview & Objectives: This course is designed for the in-depth learning of usiness Intelligence knowledge and techniques including advanced dimensional modeling, online analytic processing (OLP), data mining techniques, text mining and sentiment analysis, and social media analytics. Students will apply and integrate the business intelligence knowledge leant from IS7034 to implement a set of I tools, such as ERWin, Teradata Data Warehouse Miner and IM SPSS Data Modeler. This course also includes the case studies and a term project. The objectives of this class include: Learn advanced Dimensional Modeling Learn Data Mining Techniques with their usiness pplications Learn Text Mining and Sentiment nalysis Learn Web nalytics and Social Media nalytics Implement a set of data mining techniques such as decision tree, association rule and clustering Conduct a term project that either evaluate I tools, or survey the current status of a particular I implementation Pre-requisite: IS7034 (If you don't have this pre-requisite, please contact instructor immediately.) Textbook: This course doesn t have a required text book. course pack has been developed for you to purchase online. The ordering instructions are listed below. esides, lecture notes and reading material will be available from lackboard. "Your custom course materials for IS7034/IS7036 published by University Readers are now available online at Shipping can take 1-4 business days. Please keep in mind that our institution adheres to copyright law, so any copyrighted material should not be copied or duplicated in any manner. To purchase the textbook, please follow the instructions below: Step 1: Log on to Step 2: Create an account or log in if you have an existing account to purchase. Step 3: Choose the correct course pack, select a format and proceed with the checkout process. Step 4: fter purchasing, you can access a digital copy of the first few chapters by logging into your account and clicking "My Digital Materials" to get started on your reading right away.

2 22-IS-7034 dvanced usiness Intelligence Spring 2014 Print orders are typically processed within 24 hours; the shipping time will depend on the selected shipping method and day it is shipped (orders are not shipped on Sundays or holidays). If you experience any difficulties, please or call ext. 503." Course dministration: You are expected to come and prepare for every class by reading the weekly lecture assignments that will be posted in the lackboard. Class lectures will discuss extensively on the key concepts and ideas of each topic with additional insight. Most importantly, class lectures will clarify students questions and initiate the lively discussion. The course involves lectures, mining lab sessions, guest speaker presentations, homework assignments, one midterm exam, and a group term project. I will create an active education environment to motivate you to understand and apply the information technologies for I implementation. Therefore, attendance and class participation are critical. If your personal schedule entails missing some class sessions (more than 3 classes), please drop this course. bsence for a class does not relieve you of your responsibility for the subject matter, assignments when they are due, and other course-related issues discussed during that class period. "" student must attend all classes and actively provide thoughtful and relevant comments to class discussions. Homework ssignments, Exam, and Term Project: There is one 50-minute midterm exam. It contains both objective (e.g., T/F, fill-in, multiple choices) and essay questions. There are no scheduled make-up tests, so it is important that you take all tests at their scheduled times. If you have a problem with a test date for a valid reason like a last minute accident or illness, you must contact the instructor immediately by or phone before the actual test time. Failure to do so will mean getting a zero score. Late submissions of homework cannot be accepted unless there is a real emergency (job interviews don't qualify) and prior permission of instructor has been obtained. You may appeal any score on a test or an assignment, but such appeal must be made in writing or in person within one week from the time the test or homework is returned to the class. There is a term project and each team can have up to 5 students. Each team can work on a data warehousing, data mining, or I application project. The detailed instructions of the term project, the project report and its class presentation will be distributed as a separate handout in class. Peer evaluation will be administered to assess each team member s contribution to the project. Course Grade: Your final course grade will be determined as: 1. Homework ssignments 25% 2. Midterm Exam 30% 3. Term Project 40% 4. Professional Conduct 5% Total 100% Evaluation: The final grade will be based on the percentage of the total possible points awarded as follows: GRDE Scores GRDE Scores GRDE Scores C C C Jay Shan Page 2 of 5 IS7036 (Spring 2014)

3 22-IS-7034 dvanced usiness Intelligence Spring 2014 Communication: is the best way to communicate with me. I intend to reply your s within 24 hours. Therefore, I expect the same courtesy from you. will be a very important mode of communication in this class as we meet only once a week. lso make sure to check blackboard on a regular basis for updates or additional course material. Whenever possible, I will announce changes to blackboard course via . Class Conduct: IMPORTNT! The use of any mobile computing device, such as mobile phone, laptop, etc., during a class session is not permitted unless the instructor has approved its use. Please silence your devices and put them in your bag or jacket. ny violation can lead to the loss of one point in professional conduct! s noted on the syllabus, five percent of your final grade is dependent on the level of professionalism in your conduct during the course. One of our goals in the College of usiness is to prepare you for a professional career and we wish to instill good habits through insisting on a high level of conduct in all your classes. The following behaviors are unacceptable in this class and will result in a loss of points for professional conduct: 1. Not on time 2. Excessive tardiness/absences. 3. Leaving while class is in session for reasons other than an emergency. 4. Failing to notify the professor by when you are absent from class. 5. Working on non-course related assignments. 6. Failing to silence your cell phone. 7. Falling asleep or resting head on desk. 8. Wearing headphones while class is in session. 9. Web surfing in class 10. Other types of disrespectful behaviors. cademic Integrity: IMPORTNT! I personally take academic integrity very seriously, and if there are any violations, the consequences will be unfortunate. Please don't take this issue lightly because plagiarism in any form will not be tolerated. s with all Lindner College of usiness efforts, this course will uphold the highest ethical standards, critical to building character. Ensuing your integrity is vital and your responsibility. LC instructors are required to report NY incident of academic misconduct (e.g., cheating, plagiarism) to the college review process, which could result in severe consequences, including potential dismissal from the college. For further information on cademic Misconduct or related university policies and procedures, please see the UC Code of Conduct ( Special Needs: If you have any special needs related to your participation in this class that may influence your performance you should inform and meet the instructor immediately to arrange reasonable provisions to ensure an equitable opportunity to meet all the class requirements. Jay Shan Page 3 of 5 IS7036 (Spring 2014)

4 22-IS-7034 dvanced usiness Intelligence Spring 2014 Course Schedule (Tentative): Week Session Topics Readings & ssignments Course Logistics and Project Guideline usiness Intelligence & nalytics: Evolution Notes 1 dvanced Dimensional Modeling: Dimension Tables Ponniah: Chapter 11 Project Proposal dvanced Dimensional Modeling: Dimension Tables Ponniah: Chapter 11 2 dvanced Dimensional Modeling: Dimension Tables Ponniah: Chapter 11 Homework 1 dvanced Dimensional Modeling: Fact Tables Ponniah: Chapter 11 3 dvanced Dimensional Modeling: Fact Tables Ponniah: Chapter 11 Homework 2 4 Spring reak Pattern Mining I Han et al.: Chapter 5 Midterm Exam 5 Pattern Mining II Han et al.: Chapter 5 Project Progress Report asics of Text Mining I Miner et al: Part I 6 asics of Text Mining II Miner et al: Part I Homework 3 Web nalytics and Web Mining Miner et al.: Chapter 12 7 Social Network nalysis Notes Project Final Report 8 Term Project Presentation Term Project Presentation Jay Shan Page 4 of 5 IS7036 (Spring 2014)

5 22-IS-7034 dvanced usiness Intelligence Spring 2014 I Education Resources: I Organization and Websites: 1. The Data Warehousing Institute (TDWI): 2. IM cademic Initiates: 3. Gartner Research: 4. KDnuggets: 5. Microsoft Dynamics cademic lliance: 6. ORCLE cademy: 7. SP University lliances: 8. SS Global cademic Program: 9. Teradata Student Network: (The password is: nalytics.) 10. Walton College of usiness at University of rkansas: enterprise.waltoncollege.uark.edu/ Reference ooks: Database Systems 1. Ramez Elmasri & Shamkant. Navathe, Fundamentals of Database Systems, 6 th edition, ddison Wesley, Jeffrey. Hoffer et al., Modern Database Management, 10 th edition, Prentice Hall, 2011 Data Warehousing 1. William H. Inmon, uilding the Data Warehouse, 4 th edition, Wiley, Ralph Kimball & Margy Ross, The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2 nd edition, Wiley, Ralph Kimball et al., The Data Warehouse Lifecycle Toolkit, 2 nd edition, Wiley, Paulraj Ponniah, Data Warehousing Fundamentals for IT Professionals, Wiley, 2 nd Edition, 2010 usiness Intelligence 1. David Loshin, usiness Intelligence: The Savvy Manager s Guide, Morgan Kaufmann, Rajiv Sabherwal and Irma ecerra-fernandez, usiness Intelligence: Practices, Technologies, and Management, John Wiley & Sons, Galit Shmueli, Nitin R. Patel and Peter C. ruce, Data Mining for usiness Intelligence, Second Edition, Wiley, Efraim Turban, et al., usiness Intelligence: Managerial pproach, 2 nd edition, Prentice Hall, 2011 Data/Text Mining 1. Michael J.. erry & Jacob Kogan, Text Mining: pplications and Theory, Wiley, Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3 rd edition, Morgan Kaufmann, Gordon S. Linoff & Michael J.. erry, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3 rd edition, Wiley, Gary Miner, et al., Practical Text Mining and Statistical nalysis for Non-structured Text Data pplications, cademic Press, Sholom M. Weiss, et al., Text Mining: Predictive Methods for nalyzing Unstructured Information, Springer, 2010 Jay Shan Page 5 of 5 IS7036 (Spring 2014)