DECISION SUPPORT SYSTEMS

CSE5DSS

2018

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-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 CSE41DSS, CSE4DSS

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsBusiness Intelligence and Analytics: Systems for Decision Support (TENTH EDITION)PrescribedRamesh Sharda, Dursun Delen and Efraim TurbanPearson

Graduate capabilities & intended learning outcomes

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
Related graduate capabilities and elements:
Writing (Writing)
Discipline-specific GCs (Discipline-specific GCs)

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.
Related graduate capabilities and elements:
Writing (Writing)
Discipline-specific GCs (Discipline-specific GCs)

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.
Related graduate capabilities and elements:
Writing (Writing)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)

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

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

Subject options

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Start date between: and    Key dates

City Campus, 2018, 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 online.

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 examination70 01, 02, 03, 04, 05
one assignment equiv. to 2,500 words.30 04, 05

Melbourne, 2018, 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 examination70 01, 02, 03, 04, 05
one assignment equiv. to 2,500 words.30 04, 05