DECISION SUPPORT SYSTEMS

Course Description

This course provides a detailed understanding of decision support systems through information that supports semi-structured and unstructured decisions in many organizations. This course covers decision support systems that focus on computer-based and user manipulation of source data extracted from an organization’s internal and external databases. This course expects students to understand decision support systems such as data warehousing and data marts, online analytic processing, Data Mining, and Geographic Information Systems. This course also covers the use of modern software, including SPSS, Clementine, and the popular GIS package MapInfo. This information is available to graduate students in both business and applied Statistics and also extends to other courses.

DECISION SUPPORT SYSTEMS

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

1. 01 The student should be able to identify the main concepts of decision support systems (DSS)
2. 02 The student should be able to recognize the components of DSS and the main participants in the decision-making process
3. 03 To be able to identify the the various models and their analysis
4. 04 To be able to develop DSS and its life cycle
5. 05 To recognize the role of intelligent systems in DSS

Course Syllabus

Reference Books & Course Materials

1. 01 George M. Markas, 2011, "Decision support systems in the 21st century", 2nd edition, Pearson education

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

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