Course Syllabus for Summer 2, 2009

MIST 740 Decision Support Systems

http://alpha.nyit.edu/som/faculty/khoo/Summer2_2009/MIST740/MIST740Syllabus.htm

 

Instructor: Dr. Benjamin Khoo

Email: kkhoo@nyit.edu

Class Hours:  Tuesdays 6:00 – 10:20 pm

Room Number:  MCMB708/HSH DL4

 

Office Hours: Tentative

OW: By appointment

Manhattan: Tue & Wed 3:30 – 5:30 pm

 

1. Catalog Description


Decision-support systems (DSS) support management decision-making in a business environment. Its focus is to provide viable alternatives for managers rather than replacing judgment with an optimized solution. General topics covered include theories of organization, decision theories, inferential process, information systems, DSS software and hardware and model building. Prerequisite: MIST 705.

 

2. Expected Outcomes

This course provides an overview of Decision Support Systems (DSS)/Business Intelligence (BI), and some of the areas that they have been used successfully. The expected outcomes are:

 

  1. To introduce the development of decision support or business intelligence, and expert systems as both academic fields and as commercially viable software systems for use to support, and to automate business decision making.
  2. To enable students to acquire an understanding of the basic concepts and skills associated with decision theory and the modeling of business decisions.
  3. The enable students to recognize the different classes of decision support systems or business intelligence, expert systems, and to appreciate the different settings in which these may be used to best effect.
  4. To enable the student to appreciate the role and nature of Group Decision Support Systems and related approaches such as Cognitive Mapping as a means of structuring and supporting complex unstructured decision problems with high levels of uncertainty.
  5. To appreciate how different forms of computer system support the operation and deployment of decision support systems within organizations, and to understand how contemporary developments in Web technology are allowing these developments to diffuse into many other applications.
  6. To understand and appreciate how the fields of expert systems and artificial intelligence have evolved from earlier work in decision support systems.


3.   Email Account

Each student is expected to have a working campus email account. Students are highly encouraged to bring their notebook computers to class.

4.   Expanded Description of the Course and Instructional Methods:

This course has been developed to introduce students in business to some of the main concepts and theories that have emerged in the fields of knowledge that are now generally described as "decision support systems" or “Business Intelligence” and "expert systems". These terms are also used to refer to the two classes of computer software products that have emerged from this area of work. Decision Support Systems (DSS) represent a class of IT that is specifically designed to assist the decision maker in the current data-rich but information-poor environment. As such, DSS represents one of the most powerful classes of IT; one that consistently stretches the technological envelope and involves the integration of disparate methods, systems and applications. In today's environment, for example, DSS effectively integrate database/warehouse, online analytical processing, network collaboration, modeling, simulation, visualization, solver, knowledge and learning technologies. DSS applications have the effect of enhancing the efficacy of decisions, particularly in highly uncertain, constrained, complex and dynamic environments, as well as increasing decision-making speed, quality, traceability and consensus. In the class work students will be asked to apply their knowledge to model and give a structure to a range of decision problems. The course work will also require the students to gain experience of a limited range of computer software tools in order to better understand the practicalities of deploying such systems in real-time decision making in business settings, and their use off-line in strategy and policy development.

Instructional methods:

a.   Instructional methods used in this course include lectures, class discussions, and in-class demonstrations:

1.     Lectures are used to clarify and supplement text readings

2.     Class discussions are used to facilitate student understanding and provide integration of course material within the business educational domain

3.     Assignments provide hands-on experience with information technologies

b.   Students are expected to assimilate a portion of course content through self-study of the textbook and instructor-provided materials.

 

5.  Textbook

Decision Support and Business Intelligence Systems, 8th Edition
Efraim Turban, University of Hawaii
Jay E. Aronson, University of Georgia
Ting-Peng Liang, National Sun Yat-Sen University
Ramesh Sharda, Oklahoma State University

ISBN-10: 0131986600
ISBN-13: 9780131986602

Publisher: Prentice Hall
Copyright: 2007

 

6.   Attendance and Participation

 

Regular and punctual attendance is expected of all students. In the case of absence due to emergency (illness, death in the family, accident), religious holiday, or participation in official functions, it is the student's responsibility to confer with the instructor about the absence and missed course work.

 

7.  Examinations


There will be 2 examinations for this term. The examinations will be based on materials covered in class. They are all closed books and notes.  Makeup examinations will be allowed for only serious reasons and then they must be completed before the next class period.  An examination may never be taken early.  Only one makeup examination will be allowed.

Cheating on the assignments, during examinations or quizzes will result in removal from the course with a failing grade.  This includes crib sheets and copying from other students.  If you study together, do not sit by each other during exams!  In addition, the student will be reported to the Office of Student Discipline.  There is no second chance.

 

8.   Methods of evaluating outcomes   

Content Area

Percentage

Project

25%

2 Assignments

20% + 25%

Examination  

25%

Attendance & Participation

5%

 

9.   Assignments

Homework consists of a project and 2 assignments. All grading of deliverables will be based on standards indicated for each deliverable. Deliverables may not be turned in late! Plagiarism is defined as turning in work that is not one’s own. If the work is a duplicate of another person’s, one or both of you may be guilty of plagiarism.  The first occurrence of plagiarism will result in removal from the course with a failing grade.  In addition the student will be reported to the Office of Student Discipline.  There is no second chance.


10. Grading


Approximate (can change later!) grading scale: A = 85-100%, B = 75-84%, C = 65-74%, D = 55-64%, F < 54%

11. Useful Resources

Resources from 6th Edition

Resources from the 7th Edition

 

Decision Support Systems: A Knowledge-Based Approach (Online textbook by Clyde W. Holsapple)

 

DSS Resources Website by Daniel Power

DSS-Software by dssresources.com

DSS-Software by J.E. Aronson

DSS-Software by Vicki L. Sauter

 

Technology Evaluation Centers

 

Tentative Schedule

Lecture Dates

Material Covered

Reading

(Tentative)

Assgnmt/Exam

Powerpoint slides: http://iris.nyit.edu/~kkhoo/Summer2_2009/740-DSS/ppt

July 14

Introduction to the Course & Syllabus

Part I: Decision Support and Business Intelligence

1. Decision Support Systems and Business Intelligence

Part II: Computerized Decision Support

2. Decision Making, Systems, Modeling, and Support

3. Decision Support Systems Concepts, Methodologies, and Technologies: An Overview

Chapters 1, 2, 3

Check Resource links

Individual Project (25%)

July 21

4. Modeling and Analysis

Part III: Business Intelligence Special Introductory Section: The Essentials of Business Intelligence

5. Data Warehousing

6. Business Analytics and Data Visualization

Chapter 4, 5, 6

Assignment 1
(20%)

July 28

7. Data, Text, and Web Mining

8. Neural Networks for Data Mining

Chapter 7, 8

Project Due

August 10

9. Business Performance Management

Part IV: Collaboration, Communication, Group Support Systems, and Knowledge Management

10. Collaborative Computing-Supported Technologies and Group Support Systems

Chapter 9, 10

Assignment 1 due

August 11

11. Knowledge Management

Part V: Intelligent Systems

12. Artificial Intelligence and Expert Systems

Exam Review

Chapter 11, 12

Assignment 2
(25%)

August 18

13. Advanced Intelligent Systems

14. Intelligent Systems over the Internet

Chapter 13, 14

Exam (25%)

August 25

Part VI: Implementing Decision Support Systems

15. Systems Development and Acquisition

16. Integration, Impacts, and the Future of Management Support Systems

 Chapter 15, 16

Assignment 2 due