DECISION SUPPORT SYSTEMS

CSE4DSS

2015

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; future directions in DSS.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorJinli Cao

Available to Study Abroad StudentsYes

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjects CSE41DSS

Equivalent subjects CSE41DSS

Special conditionsN/A

Graduate capabilities & intended learning outcomes

01. By the end of this subject students will be able to explain the fundamental terms, concepts and theories associated with decision support systems.

Activities:
Students attend a two-hour lecture in which they are introduced to the fundamental terms, concepts and theories associated with DSS. In the 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.
Related graduate capabilities and elements:
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)

02. By the end of this subject students will be able to describe the components of Decision Support Systems architectures, and how these components integrate.

Activities:
Students attend a lecture which covers decision support system architecture. In the practice class students provide written answers to questions based on one or more case studies.
Related graduate capabilities and elements:
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)

03. By the end of this subject students will be able to describe the phases of the systematic decision making process, and to 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.
Related graduate capabilities and elements:
Writing (Writing)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)

04. By the end of this subject students will understand the basic concepts of optimization, simulation and heuristic models, and be able to construct spreadsheet solutions to some 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.
Related graduate capabilities and elements:
Writing (Writing)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

05. By the end of this subject students will understand the objectives and benefits of business analytics and data mining, and be able to apply a range of data mining techniques to real-world datasets.

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.
Related graduate capabilities and elements:
Writing (Writing)
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Critical Thinking (Critical Thinking)

Subject options

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Start date between: and    Key dates

Melbourne, 2015, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAndrew Skabar

Class requirements

Laboratory Class Week: 10 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

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.

Assessments

Assessment elementComments% ILO*
one 3-hour examination70 01, 02, 03, 04, 05
one assignment equiv. to 1,500 words.30 04, 05

Melbourne, 2015, Summer, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAndrew Skabar

Class requirements

Lecture
Two 1.0 hours lecture per week on weekdays during the day and delivered via face-to-face.

Laboratory Class
One 2.0 hours laboratory class per week on weekdays during the day and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
one 3-hour examination70 01, 02, 03, 04, 05
one assignment equiv. to 1,500 words.30 04, 05