ISQS 3358-001 (19903), Fall 2016


Business Intelligence


Focuses: Dimensional modeling, Data warehousing, ETL, OLAP, Visualization, Hadoop & MapReduce


Class meetings: TR 12:30-1:50p, BA103

Instructor: Zhangxi Lin, (806) 834-1926, Email: zhangxi.lin at ttu

Office hours: TR 10:00a-12:00p BA E311, or by appointment

Social networking: LinkedIn – my TTU email, Facebook – Zhangxi.lin at gmail


Teaching Assistant: Jingjing Liu, jingjing.liu at, Partha Satpathy, partha.s.satpathy at, office hours: MW 2-3:30p


Homework submission:


(This syllabus is subject to further modification)


Home | Schedule | Projects | Job search | Quiz reviews  | Exercises | Attendance 


Course description:

Business intelligence (BI) is referred to as applications and technologies which are used to gather, provide access to, and analyze data and information about their company operations. Business intelligence systems can help companies have a more comprehensive knowledge of the factors affecting their business, such as metrics on sales, production, internal operations, and they can help companies to make better business decisions. This course is to be based on the contents covered by database management to further train students the skills methodologies, and knowledge how to accomplish data management tasks with the applications of BI tools and techniques. In particular, topics in big data, such as Hadoop/MapReduce, are extended to cope with demand from the job market. The main components of this course include:

  • Data warehousing
    • Dimensional data model
    • Information integration and flow design in the ETL (extraction, transformation, and loading) process 
    • Online analytical processing (OLAP)
  • Data analysis
    • Data reporting and query techniques 
    • Data visualization
    • Principle of data mining
  • Introduction to big data

Prerequisites: Database, Statistics


Learning objectives:

  • To learn the basic principles of big data oriented business intelligence
  • To be able to design and construct a data warehouse
  • To develop basic data processing skills, specifically ETL system implementation.
  • To be able to perform OLAP tasks
  • To develop general data visualization skills


Assessment of Learning:

  • General principles will be assessed through quizzes and the final exam.
  • Data warehouse design, ETL system implementation, and OLAP task abilities will be assessed through quizzes, final examination and homework assignments.
  • General big data processing and data analysis skills will be assessed through exercises and homework assignments.


Teaching style: Case-based hands-on learning process.


Software tools to be used: Microsoft SQL Server 2008 R2, SAS Enterprise Guide V4.2, Pentaho/Tableau, Hadoop


Required textbook: 

  • Querying and Reporting Using SAS® Enterprise Guide®

  (Online text: )


Optional textbook:

Print: ISBN-10 0-13-610066-X, ISBN-13 978-0-13-610066-9

eText: ISBN-10 0-13-610067-8, ISBN-13 978-0-13-610067-6

Or the older version, if available, Business Intelligence, Pearson Prentice Hall, 2008, ISBN-10: 013234761X, Turban et al

·         Delivering Business Intelligence with Microsoft SQL Server 2008, by Brian Larson, Publisher: McGraw-Hill Osborne Media; November 19, 2008, ISBN 0071549447 / 9780071549448

·         Supplemental handouts for big data topics


Term project and project groups:


A data warehousing project will be accomplished by each team composed of up to three students. Deliverables include a team presentation of 15 minutes and a term report in 6-10 pages.


In-class Exercises:


There are three sets of exercises conducted in the classroom.

1)     Data warehousing with SQL Server – 8 exercises

2)     Data visualization and analysis – 5 exercises


Homework Assignments


Homework is mainly the assigned readings or small projects after each class meetings, and will not be graded. Quizzes may cover some contents of the assignments.



  • Five quizzes out of six. One will be dropped whichever has the lowest score (80 points, no make-up test)
  • Exercises (60 points)
  • One term project and team presentations (60 points)
  • Open-book final Exam (50 points)
  • Attendance (10 points)

The total is 260 points

Extra bonus credit: up to 10 points based on the evaluation of the involvement in the class activities.


A – 90% or higher & overall a B or upper in exams & good attendance

B – 80-89.9% & overall a C or upper in exams

C – 70 – 79.9% & overall a C- or upper in exams

D – 60 – 69.9%

F < 60%



It is highly suggested that students attend all class meetings, particularly because of tight course schedule. The attendance is counted as 10 points and the roll check will be taken randomly. Missing one class is fine but will lose 5 points for missing each extra meeting. 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 unexpected situation, the evidence must be presented to the instructor for the credit of the attendance points.


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.


·         Dimensional Modeling: In a Business Intelligence environment, IBM, 2006

·         Pentaho website

·         Tableau

·         R programming References

·         Big Data: Understanding How Data Powers Big Business, Bill Schmarzo, Wiley, ISBN: 978-1-118-73957-0, 240 pages, October 2013

·         The Microsoft Data Warehouse Toolkit, Joy Mundy and Warren Thornthwaite, Wiley, 2006, ISBN 0-471-26715-5

·         Hands-On Microsoft SQL Server 2008 Integration Services, by Ashwani Nanda, Second Edition, McGraw Hill Osborne, September 2, 2010, ISBN 0071736409 / 9780071736404

·         Business Intelligence: The Savvy Manager's Guide; Morgan Kaufmann (June 2003); ISBN: 155860916

·         Textbook website: or

·         Online SAS references


Selected online resources: