ISQS 6339, Spring 2015

 

Data Management and Business Intelligence

 

Focuses: Dimensional modeling, ETL, OLAP, Data processing for analytic tasks, Introductory big data

 

Class meetings: TR 2:00-4:50p, BA287

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

Office hours: TR 11a-11p, BA 003 (subject to change), or by appointment at BA E311

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

 

Teaching Assistant (TBD):

 

Homework submission: isqs6347@gmail.com

 

(This syllabus is subject to further modification)

 

Home | Schedule | Projects | Job search | Class Notes | Video Demos | Exercises

 

Course description:

Data management comprises all the disciplines related to managing data as a valuable resource. 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 this year, topics in big data, such as Hadoop/MapReduce, are added to cope with demand from the job market. The main components of this course include:

  • Big data concepts
  • Hadoop & MapReduce
  • Principles of data warehousing
  • Dimensional data model
  • Information integration and flow design in the ETL (extraction, transformation, and loading) process 
  • Online analytical processing (OLAP)
  • Data reporting and query techniques 
  • Data preparation for analytics
  • Distributed data warehousing in the cloud computing platform

The course is lectured with cases plus in-class exercises.

 

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 conduct OLAP tasks
  • To develop general data preparation skills for analytic tasks
  • To be able to plan a cloud computing 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 data preparation skills will be assessed through quizzes, in class exercises, and homework assignments.

 

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

 

Software tools to be used: Microsoft SQL Server 2016, R Services, SAS Enterprise Guide V4.2, Tableau

 

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

 

Project and the project group:

 

There will be seven project teams in the class, each with no more than 5 but no less than 3 members. Project team will accomplish two data warehousing projects and present a selected big data topic in the class.

 

In-class Exercises:

 

There are 13-15 exercises. Exercises could be one to two short answer questions or BI assignments to be completed in the class. All are open-book and open notes. Most of the exercises will be handed in for grading. The total credit for these exercises is 120 points. Students may be allowed to skip up to two in-class exercises without losing any credit. Missing any extra exercise will cost 10 points. 

 

Homework

 

Homework is mainly the assigned readings or small projects after each class meetings, and will not be graded. These assignments will be covered in class discussions and in quizzes.

 

Grading:

  • Five quizzes out of Six (80 points, no make-up test)
  • Exercises (60 points; 100% for A+, 90%+ for A, 80%+ for B)
  • Two Data Warehousing Projects (60 points, 100% for A+, 90%+ for A, 80%+ for B)
  • Big data collaborative studies (20 points)
  • Open-book final Exam (80 points)

The total is 300 points

Extra bonus credit: up to 15 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%

 

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.

 

SAS Certificate Exams:

 

References:

 

Selected online resources:

Note: Search on monster.com you can see the number of available BI job positions is “> 5000”