Syllabus

 

ISQS 7339, Business Analytics, fall 2015

Class Meeting: MW, BA003, Session 1: 15:00-16:20, Session 2: 16:30-17:50

Instructor: Zhangxi Lin

Teaching Assistant: Shengbin Lin, shengbin.lin at ttu.edu

Office hours: 14:00-15:00 MW & 14:00-16:00 T, 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 four topics:

 

1.     Text mining

2.     Decision tree algorithm implementation

3.     Advanced predictive modeling techniques

4.     An introduction to social network analytics

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 big data processing

4.     Basic social network analysis skills.

 

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, exams, and term project.

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

3.     The knowledge in conduct market and customer analytics will be assessed by in-class exercises, exams, and term project.

4.     The skills in analyzing social network will be accessed by in-class exercises, and term project

 

Required textbooks:

1.     Mining Textual Data Using SAS® Text  Miner, 328p, (DMTM)

2.      Decision Tree Modeling, SAS Course Notes (DMDT)

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

4.     Social Media Mining - An Introduction, Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, Cambridge University Press, April 20, 2014 (SMM), e-Book

 

Optional:

·         Decision Trees for Analytics Using SAS Enterprise Miner, By: Padraic Neville; Barry de Ville, Publisher: SAS Institute, Pub. Date: July 10, 2013, ISBN-13: 978-1-62959-100-1, p268, e-Book.

·         Mining the Social Web, Matthew A. Russel, O’Railly Media, Second Edition, Oct 20, 2013, ISBN: 1449367615/978-1449367619, e-Book

Grading Policy:

  • 7 quizzes and one final exam, equally weighted,, one of which will be dropped whichever with the lowest score (140 points)
  • Exercises (40 points)
  • Team projects and presentation (100 points).

The total is 280 points.

Bonus credit is up to 20 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 10 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 quizzes are closed book and closed notes. Students are required to take the test in the classroom at the scheduled date and time. Most questions will be chosen based on exercises, textbooks, and/or the supplemental materials distributed in the class. There is no make-up test for any quizzes.

Assignments:

In-class exercises are guided in the classroom but some of them can be of take-home type. Timeliness and completeness of submission are two main criteria for the score.

Homework assignments will; be available for students to review the lectures but not counted into the credit.  Homework assignments must be completed by the due date to be legible for grading and commenting.

Project:

Master students: A semester-long project is accomplished by team work. Each team consists of no more than 5 students. More information will be announced.

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

 

References:

·         Social Big Data Mining, By: Hiroshi Ishikawa, Publisher: CRC Press, Pub. Date: April 2, 2015, Pages in Print Edition: 200, e-Book

·         Social Data Analytics, By: Krish Krishnan; Shawn P. Rogers, Publisher: Morgan Kaufmann, Pub. Date: November 10, 2014, Print ISBN-13: 978-0-12-397186-9, Web ISBN-13: 978-0-12-397780-9, Pages in Print Edition: 158, e-Book

·         Introduction to Data Mining,  Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Addison Wesley, 2005, ISBN: 0321321367 (Website: http://www-users.cs.umn.edu/~kumar/dmbook/index.php )

·         Data Mining – A Tutorial Based Primer, Richard Roiger, Michael Geatz, 3rd edition. Addison Wesley, 2003, ISBN 0201741288

·         Online SAS references

·         New versions of SAS Enterprise Miner

 

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.

Regulations of sexual assault and harassment. Texas Tech University is dedicated to providing a safe and equitable learning environment for all students. Discrimination, sexual assault, and harassment are not tolerated by the university. You are encouraged to report any incidents to The Student Resolution Center: (806) 742-SAFE (7233). The TTU Counseling Center (http://www.depts.ttu.edu/scc/) provides confidential support (806-742-3674) and the Voices of Hope Lubbock Rape Crisis Center has a 24-hour hotline: 806-763-RAPE (7273). For more information about support, reporting options, and other resources, go to: http://www.depts.ttu.edu/sexualviolence/

Avoiding Conflict: Cheating and Plagiariam, see http://www.depts.ttu.edu/studentresolutioncenter/conflict/cheating.php