DECISION SUPPORT SYSTEMS

CSE5DSS

2020

Credit points: 15

Subject outline

This subject covers the fundamental terms, concepts and theories associated with decision support systems (DSS), and provides practical experience in applying a range of current modelling and data analysis tools. Specific topics include: decision support systems and business intelligence; understanding the process of decision making; modelling and analysis techniques for decision support; data mining for business intelligence; text and web mining; artificial intelligence and expert systems for decision support, and recommender systems.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAndrew Skabar

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjects CSE4DSS

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsBusiness Intelligence and Analytics: Systems for Decision Support, Tenth editionPrescribedRamesh Sharda, Dursun Delen and Efraim Turban,2014Pearson

Graduate capabilities & intended learning outcomes

01. Locate the position of common business decision problems on the Gorry and Scott-Morton framework, and identify appropriate decision support technologies that can be applied to those problems.

Activities:
In the opening two lectures students are introduced to the fundamental terms, concepts and theories associated with DSS. The Gorry Scott-Morton Framework is introduced, and students complete an exercise in mapping common decision problems onto this framework. As new technologies are introduced to students through the course of the subject, reference is made to the types of problems to which these technologies can be applied.

02. Explain 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. Design and create linear programming models to solve optimisation problems such as product-mix, scheduling, and portfolio optimisation problems

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. Design and create Monte Carlo simulation models for problems where an exact solution cannot be found, such as inventory management and scheduling problems

Activities:
Two lectures are used to introduce students to Monte Carlo simulation. In the accompanying practice class, students complete an exercise in creating a simulation model for a maintenance scheduling problem.

05. Select and apply appropriate data analysis techniques to make predictions based on real-world data, 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.

Subject options

Select to view your study options…

Start date between: and    Key dates

Melbourne, 2020, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAndrew Skabar

Class requirements

Lecture Week: 10 - 22
Two 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Computer Laboratory Week: 11 - 22
One 2.0 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
One 3-hour examination, equiv. to 3,000 words.70 01, 02, 03, 04, 05
Assignment 1, equiv. to 1,000 words.Students solve problems using linear programming and simulation, and report on their findings.15 03, 05
Assignment 2, equiv. to 1,000 words.Students use a variety of data analysis techniques to solve a range of problems, and report on their findings.15 05