Syllabus

 

ISQS 7339, Business Analytics, fall 2012

Class Meeting: TR, BA 287; 9:30-10:50a

Instructor: Zhangxi Lin

Office hours: 2:00-3:30p TR, BA E311, or by appointment

 

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Course Description:

This course is designed for those who intend to further improve their knowledge of data analysis in both theoretic and practical aspects. The course is to cover three topics:

 

1.    Decision tree algorithm implementation

2.    Advanced predictive modeling techniques

3.    An introduction to sentiment analysis and opinion mining

4.    Market segmentation with clustering modeling

Students will further master the skills of advanced modeling with SAS Enterprise Miner 6.1 based on previously learned data mining techniques. A number of cases in business analytics, such as risk management, market analysis, and online advertising, will be presented.  Class discussions will be conducted to motivate the involvement of students.

 

Prerequisite: ISQS 6347, Data and Text Mining

 

Learning Outcomes:

A student who successfully completes this course should be able to:

1.    Solve business decision problems with advanced data mining techniques using SAS Enterprise Miner

2.    Refine predictive models involving imperfect data

3.    Master the principles of sentiment analysis and opinion mining

4.    Apply the data mining approaches in market segmentation with either SAS Enterprise Miner or SAS Enterprise Guide

 

Assessment of Learning Outcomes:

     Learning will be assessed as follows:

1.    The ability to solve business decision problems will be assessed by in-class exercises, homework, exams, and/or a term project.

2.    The skills in refining predictive models involving imperfect data will be assessed by in-class exercises, homework, exams, and/or a term project.

3.    The ability to conduct market segmentation with data mining approaches will be assessed by in-class exams, homework, exams, and/or a term project.

 

Required textbooks:

Decision Trees for Business Intelligence and Data Mining, (Preview the book, e-Book)

Barry de Ville, SAS Press, October 2006, ISBN: 978-1-59047-567-6**

E-Books:

Decision Tree Modeling, SAS Course Notes (DMDT)

Advanced Predictive Modeling Using SAS® Enterprise Miner, SAS Course Notes (PMAD)

Customer Segmentation with Numeric and Textual Data Using SAS, SAS Course Notes (GACS)

Effective Web Mining: Attracting and Keeping Valued Cyber Consumers, SAS Course Notes

 

References:

CRM Segmentation and Clustering Using SAS Enterprise Miner, (Preview the book), Randall S. Collica, SAS Press, 2007, ISBN: 978-1-59047-508-9**

Using SAS in Financial Research, By: Ekkehart Boehmer, John Broussard, and Juha Pekka Kallunki, 184 pages ISBN: 978-1-59047-039-8Description: Description: Description: Description: Description: https://support.sas.com/pubscat/images/global/clear.gif Publisher: SAS Press, Date: February 2002

Sentiment Analysis and Opinion Mining (Synthesis Lectures on Human Language Technologies) by Bing Liu (May 23, 2012), ISBN-10: 1608458849, ISBN-13: 978-1608458844

Grading Policy:

  • One midterm exam (70 points)
  • One open-book final exam. (70 points)
  • Six quizzes (50 points), one of which will be dropped whichever with the lowest score.
  • Assignments (60 points)
    1. Homework assignments (30 pints)
    2. Class exercises (20 points)
    3. E-learning assignments (10 points)
  • Term project (100 points, including 40 points for group presentation)
  • Class involvement and Attendance (20 points)

The total is 370 points.

Bonus credit is up to 30 points.

Attendance:

Previous studies showed that a student’s performance in a course is closely correlated to his/her class attendance. It is highly suggested that students attend all class meetings. The attendance is counted as 20 points and the roll check will be taken randomly. Missing one or two classes will lose 5 points for each. Missing more than two classes will result in no credit from the attendance. If a student has to skip a class meeting, he/she needs to inform the instructor in advance. If the absence was caused by an expected situation, the evidence must be presented to the instructor for the credit of the attendance points.

Examinations:

The exams will be closed book and closed notes. You are required to take the test in the classroom at the scheduled date and time, unless you have disabling conditions. In which case, alternative place and time may be discussed. Most questions will be chosen from the questions at the end of the chapters and from the supplemental materials distributed in the class.

Make-up test may be available in the case that you could not take the test at the specific time due to medical emergencies or unexpected travel plans. If you need to reschedule a test, you must contact the instructor before the scheduled test and show the instructor the evidence of the excuse later, for example, a written note from a doctor for the medical problem.

There are six quizzes. There is no make-up test for any quizzes.

Homework assignments:

There are six homework assignments, worth 30 points. Homework assignments must be completed by the due date to be legible for grading. Late submission without good excuse will result in lower credit.

Project:

Master students: A semester-long project is accomplished by every individual student. Each student will deliver a final report for business problem solving focusing on data preparation and data analysis.

PhD students: PhD students will fulfill a report/paper with another student or individually.

 

University Policies:

Requirements:  Please contact me if you have any special requirements, or if I need to make special accommodations for you during the semester.  I encourage you to visit with me about your progress in the course at any time.

Integrity.  Academic dishonesty will not be tolerated.  All students are required to adhere to the Texas Tech University Policy on Academic Honesty

Civility in the Classroom.  “Students are expected to assist in maintaining a classroom environment which is conducive to learning.  In order to assure that all students have an opportunity to gain from time spent in class, unless otherwise approved by the instructor, students are prohibited from using cellular phones or beepers, eating or drinking in class, making offensive remarks, reading newspapers, sleeping or engaging in any other form of distraction.  Inappropriate behavior in the classroom shall result in, minimally, a request to leave class.” 

ADA Requirements.  Classroom accommodations will be made for students with disabilities, if requested.

Religious Holidays.  A student who intends to observe a religious holy day should make that intention known to the instructor prior to an absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence.

 

References: