Decision Support Systems


  • GENERAL
SCHOOL Faculty of Social, Political and Economic Sciences
ACADEMIC UNIT Department of Economics
LEVEL OF STUDIES Undergraduate
COURSE CODE ΝΕ88 SEMESTER 8th
COURSE TITLE Decision Support Systems
INDEPENDENT TEACHING ACTIVITIES
if credits are awarded for separate components of the course, e.g. lectures, laboratory exercises, etc. If the credits are awarded for the whole of the course, give the weekly teaching hours and the total credits
WEEKLY TEACHING HOURS CREDITS
4 6
 
 
Add rows if necessary. The organisation of teaching and the teaching methods used are described in detail at (d).
COURSE TYPE

general background,
special background, specialised general knowledge, skills development

Special Background
PREREQUISITE COURSES:

 

LANGUAGE OF INSTRUCTION and EXAMINATIONS: HELLENIC
IS THE COURSE OFFERED TO ERASMUS STUDENTS YES (ESSAY IN ENGLISH)
COURSE WEBSITE (URL)

 

 

  • LEARNING OUTCOMES
Learning outcomes
The course learning outcomes, specific knowledge, skills and competences of an appropriate level, which the students will acquire with the successful completion of the course are described.

Consult Appendix A

·     Description of the level of learning outcomes for each qualifications cycle, according to the Qualifications Framework of the European Higher Education Area

·     Descriptors for Levels 6, 7 & 8 of the European Qualifications Framework for Lifelong Learning and Appendix B

·     Guidelines for writing Learning Outcomes

The course introduces the student to the field of decision support methods and systems.

 

The learning outcomes are as follows:

– Ability to perceive the characteristics of the decision models in real time or not.

– Ability to locate and select appropriate data to support decision models.

– Ability to analyze, investigate and evaluate a decision model.

– Ability to analyze and draw conclusions on:

o Characteristics and variables in the standardization of decision models

o The characteristics and type of data required to develop and support decision models

o Characteristics and methodological approach to developing decision support systems

Upon completion of the course, students should be able to:

– Know basic principles, methodologies and features in decision making models

– Understand the features of decision support systems on the basis of:

o Big Data Management, (Big Data)

o Selecting appropriate data, (Data Mining)

o Development of Management Information Systems, (MIS)

o Decision Support Systems Development (DSS).

General Competences
Taking into consideration the general competences that the degree-holder must acquire (as these appear in the Diploma Supplement and appear below), at which of the following does the course aim?
Search for, analysis and synthesis of data and information, with the use of the necessary technology

Adapting to new situations

Decision-making

Working independently

Team work

Working in an international environment

Working in an interdisciplinary environment

Production of new research ideas

Project planning and management

Respect for difference and multiculturalism

Respect for the natural environment

Showing social, professional and ethical responsibility and sensitivity to gender issues

Criticism and self-criticism

Production of free, creative and inductive thinking

……

Others…

…….

Search for, analysis and synthesis of data and information, with the use of the necessary technology

Working independently

 

  • SYLLABUS
The content of the course includes the following sections:

i. Data management for the development of decision models:

a. Data Mining

b. Data Blending

c. Big Data

ii. Introduction to Management Information Systems:

a. Management Information Systems (MIS)

b. Geographic Information Systems (GIS)

c. Data Visualization Systems (DVS)

d. Negotiation Support Systems (NSS)

e. Decision Support Systems (DSS)

 

  • TEACHING and LEARNING METHODS – EVALUATION
DELIVERY
Face-to-face, Distance learning, etc.
Face-to-face
USE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Use of ICT in teaching, laboratory education, communication with students
E-mail and e-class
TEACHING METHODS

The manner and methods of teaching are described in detail.

Lectures, seminars, laboratory practice, fieldwork, study and analysis of bibliography, tutorials, placements, clinical practice, art workshop, interactive teaching, educational visits, project, essay writing, artistic creativity, etc.

 

The student’s study hours for each learning activity are given as well as the hours of non-directed study according to the principles of the ECTS

Activity Semester workload
Lectures 52
Assignments 20
Independent study 78
Course total 150
STUDENT PERFORMANCE EVALUATION

Description of the evaluation procedure

 

Language of evaluation, methods of evaluation, summative or conclusive, multiple choice questionnaires, short-answer questions, open-ended questions, problem solving, written work, essay/report, oral examination, public presentation, laboratory work, clinical examination of patient, art interpretation, other

 

Specifically-defined evaluation criteria are given, and if and where they are accessible to students.

Written examination (70%):

– Assignments (30%)

 

 

 

 

 

 

  • ATTACHED BIBLIOGRAPHY
– Suggested bibliography:

1.     Ι.Κ.Μουρμούρης, «Εφαρμογές Θεωρίας Αποφάσεων Πολλαπλών Κριτηρίων: Μεταφορές, Χωροθέτηση και Ανάπτυξη», ISBN 9789603516880, Εκδόσεις: Α. Σταμούλης, 2007.

 

2.     Ν. Ματσατσίνης – Κ. Ζοπουνίδης, “Συστήματα αποφάσεων με πολλαπλά κριτήρια”, ISBN 9604610686, Εκδόσεις: Κλειδάριθμος, 2007.

 

– Related academic journals:

International Journal of Management and Decision Making

International Journal of Decision Support Systems

Decision-Making for Supply Chain Integration-Springer

International Journal of Multicriteria Decision Making

Multiple Criteria Decision Making-Springer

Journal of Multi-Criteria Decision Analysis

Decision Support Systems

Journal of Decision Systems

Journal of Soft Computing and Decision Support Systems