01. explain the fundamental terms, concepts and theories associated with decision support systems

Activities:

In the opening lectures students are introduced to the fundamental terms, concepts and theories associated with DSS. In a two-hour practice class, students provide written answers to questions based on one or more case studies, and also complete an Internet-based activity in which they are required to explore a number of web sites containing various DSS-related materials and resources

02. describe the phases of the systematic decision making process, and identify these phases in case studies based on real world business scenarios

Activities:

Students attend a lecture which covers Simon's four phases of decision-making: intelligence, design, choice, and implementation. In the practice class students provide written answers to questions based on one or more case studies.

03. explain the objectives and benefits of business analytics and data mining, distinguishing between descriptive, predictive and prescriptive analytics

Activities:

The basic ideas relating to business analytics are presented to students in lectures. Practice classes focus on providing students with practical skills in the application of predictive and prescriptive analytics through activities involving the construction of optimization, simulation and heuristic models; data mining using classification, clustering and association rules algorithms; and the development of small expert systems.

04. demonstrate understanding of the basic concepts of optimization, simulation and heuristic models by being able to construct spreadsheet solutions using such models

Activities:

The basic theory behind the various models is initially presented to students in lectures. In practice classes, students use a spreadsheet software package to model a variety of commonly encountered business problems.

05. apply a range of data mining techniques to real-world datasets, justifying the appropriateness of a technique to a particular problem scenario, and critically evaluating results

Activities:

The basic theory relating to data mining and web mining is initially presented to students in lectures. In practice classes, students use a data mining package such as WEKA to apply commonly used data mining techniques such as classification, clustering, and association rule discovery to a variety of synthetic and real-world datasets. They also examine a number of case studies.