List of Courses

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Outcomes
Contents
Methodologies
Assessment
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Decisions Support Systems

Course Code :
IS433
Level :
Undergraduate
Course Hours :
3.00 Hours
Department :
Department of Information Systems

Instructor information :

Title:Professor
Name: AMIRA MOHAMMED IBRAHIM IDREES
Office hours:1-Hours

Title:Teaching Assistant
Name: Maha Farghaly Ali Ahmed
Office hours:1-Hours

Area of Study :

Explain different kinds of decision support systems and their functions. Explain how decision support systems can be used in different kinds of organizations. Analyze a typical decision situation to evaluate different alternatives. Use effectively communication skills. Explain how decision support systems can be used in different kinds of organizations and how computing is utilized as a management tool which is an integral part of the management decision making process. Implement and evaluate information technology-based systems that support managerial and business functions, data-driven DSS and model-driven DSS. Illustrate the operational, strategic and practical issues in decision support systems.

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Decisions Support Systems

This course covers the following topics: Introduction to decision support systems; DSS components; Decision making and DSS; DSS software and hardware; developing DSS; DSS models; types of DSS; group DSS; executive information systems; data mining; artificial intelligence and expert systems. It is devoted to introduce decision support systems; show their relationship to other computer-based information systems, demonstrate DSS development approaches, and show students how to utilize DSS capacities to support different types of decisions.

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Decisions Support Systems

NAQAAABET

Course outcomes:

a. Knowledge and Understanding:

1-	Illustrate the basic components, types and methods of computer-based Decision Support Systems (DSS)
2-	Operate and develop appropriate context and applications of DSS tools and techniques
3-	Identify different computer-aided data management and modeling tools for DSS, and explain basic features and applications of two DSS model Building Languages.
4-	Describe decision support techniques.

b. Intellectual Skills:

1-	Demonstrate the role of DSS in different fields
2-	Analyze different examples of decision support systems to alternative decision situations, and apply DSS tools and methods to different decision problems.
3-	Select appropriate DSS tools for application in specific situation.
4-	Evaluate and verify the productivity and efficiency of alternative DSS modeling Languages.

c. Professional and Practical Skills:

1-	Apply data retrieval techniques.
2-	Analyze decision problems and select appropriate analytical tools.
3-	Execute different systems in terms of general quality attributes and possible tradeoffs presented within the given problem.

d. General and Transferable Skills:

1-	Work in a team effectively and obtain problem solving skills.
2-	Apply oral communication skills and techniques in report writing.

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Decisions Support Systems

Course topics and contents:

Topic	No. of hours	Lecture	Tutorial/Practical
Decision making, Systems, Modeling and Computer Support, Decision Support Systems (DSS) - An Overview	4	2	2
Application, experience and Impact of DSS	4	2	2
Data-centered Decision Support Systems	4	2	2
Data preprocessing and Extraction	4	2
Quiz	2
Data Analysis and Visualization	4	2	2
Model-centered Decision Support Systems	4	2	2
Optimization Models	4	2	2
Mid-Term Exam	2	2	2
Predictive Models	4	2	2
Descriptive Models	4	2	2
Developing DSS using visual Decision Support Language (VDSS)	4	2	2
Developing DSS using General Algebraic Modeling System (GAMS)	4	2	2
Final Exam	2

For further information :

Telephone: 16383 (16FUE)
E-mail: [email protected]

Decisions Support Systems

Teaching And Learning Methodologies:

Teaching and learning methods
Interactive Lectures including Discussions
Practical Lab Sessions
Self-Study (Project / Reading Materials / Online Material / Presentations)
Case Studies