ISQS 3358-002 (53931), Spring 2016

 

Business Intelligence

 

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

 

Class meetings: MWF 1:00-1:50p, BA103

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

Office hours: MW 17:30-19:00 BA E311, or by appointment

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

 

Teaching Assistant: Tiju George

 

Homework submission: isqs3358@gmail.com

 

(This syllabus is subject to further modification)

 

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Important: Rawls College 2016 Symposium on Big Data, April 29, 2016, McCoy Atrium, The Rawls College, TTU

 

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)
  • Big data
    • Hadoop & MapReduce
    • Big data management
    • Big data applications
  • Data analysis
    • Data reporting and query techniques 
    • Data visualization
    • Principle of data mining

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
  • To be able to set up a Hadoop based business intelligence system

 

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: http://support.sas.com/documentation/onlinedoc/guide/tut42/en/menu.htm )

 

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:

 

There is a big data project based on team work. Students will build up a Hadoop system and explore/visualize a Hadoop based data warehouse. Students are divided into three cohorts. Cohort 1 uses Pentaho for data analysis, Cohort 2 uses Tableau for data analysis, and the third cohort will work on self-selected business intelligence topic. Each cohort may home 2-4 teams with no more than 12 students in total, and each team is composed of 2-4 members. 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 – 7 exercises

2)     Big data hands-on tasks – 4 exercises

3)     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.

 

Grading:

  • Five quizzes out of six. One will be dropped whichever has the lowest score (80 points, no make-up test)
  • Exercises (80 points; 100% for A+, 90%+ for A, 80%+ for B)
  • One big data project (50 points, 100% for A+, 90%+ for A, 80%+ for B)
  • Open-book final Exam (60 points)
  • Attendance (20 points)

The total is 280 points

Extra bonus credit: up to 20 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%

 

Attendance:

It is highly suggested that students attend all class meetings, particularly because of tight course schedule. The attendance is counted as 20 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.

References:

 

Selected online resources: