The course establishes a foundation for understanding and analysing information and information systems in organisations. It also provides an overview of technical and organisational aspects of decision support systems (DSS), including individual, group and organisational DSS as well as executive information systems (EIS). Management of DSS and EIS within the end-user computing environment is also discussed. The course covers more recent technologies, e.g. Data Warehouse and OLAP-technologies. The course is design-oriented and emphasises conceptual foundations of DSS and EIS, but DSS software reviews, demonstrations, laboratory lessons and case examples are also included. Mini Project.
Intended learning outcomes
Knowledge &understand
That the student recognize the general meaning of decision support systems and interpret the relationship between information and decisions
That the student recognize the types of administrative decisions and the decision-making phases
That the student recognize the components, classification, functions and characteristics of decision support systems
That the student recognize the meaning of models and the types of models used in decision support systems
That the student recognize the meaning of business intelligence and interpret the difference between business intelligence and decision support systems
That the student recognize the meaning of a data warehouse and interpret the difference between operational databases and data warehouses
That the student recognize the general meaning of data mining technology, its methods and application
That the student recognize the general meaning of Online Analytical Processing (OLAP) technology, its characteristics and its working mechanism
That the student recognize the general meaning of data visualization technology and its most famous types
mental skills
That the student link the between information and decisions
That the student concludes the preference of using decision support systems in decision-making
That the student distinguish between operational databases and data warehouses
That the student conclude the importance of using analysis techniques (data mining, online analytical processing, data warehouses, data visualization) in analyzing big data and assisting in decision-making
Practical & professional skills
That the student diagnose any problem he faces (structured, semi-structured, unstructured(
That the student use analysis techniques (data mining, online analytical processing, data visualization) to help him make decisions
General and transferable skills
The student should be able to use modern technological tools
The student should be able to communicate in writing and oral
The student should be able to write scientific reports and articles
The student should be able to make oral presentations
Teaching and learning methods
Lectures
Tutorials
Panel discussions
Mini-projects
Methods of assessments
Midterm exam = 40
Mini-projects = 10
Final exam = 50
Course contents
Decision support systems Definition – Information and Decisions –Management and Decision-Making
Types of Managerial Decisions – Decision-Making Phases
Components of decision support systems – Classification of decision support systems
Functions, Activities and Characteristics of decision support systems
Models in decision support systems
Business intelligence – The difference between business intelligence and decision support systems
Data warehouses
The difference between operational databases and data warehouses
CRISP-DM )Cross-Industry Standard Process for Data Mining)
Online Analytical Processing (OLAP)
Operations and types of Online Analytical Processing