DECISION SUPPORT SYSTEMS

CSE5DSS

2021

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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorAndrew Skabar

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjectsCSE4DSS

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Business Intelligence and Analytics: Systems for Decision Support

Resource TypePrescribed

Resource RequirementN/A

AuthorRamesh Sharda Dursun Delen and Efraim Turban

Year2014

Edition/VolumeN/A

PublisherPearson

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

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.
02. Explain the phases of the systematic decision making process, and identify these phases in case studies based on real world business scenarios
03. Design and create linear programming models to solve optimisation problems such as product-mix, scheduling, and portfolio optimisation problems
04. Design and create Monte Carlo simulation models for problems where an exact solution cannot be found, such as inventory management and scheduling problems
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

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorAndrew Skabar

Class requirements

Computer LaboratoryWeek: 11 - 22
One 2.00 h computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.

LectureWeek: 10 - 22
Two 1.00 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
One 3-hour examination, equiv. to 3,000 words.N/AN/AN/ANo70 SILO1, SILO2, SILO3, SILO4, SILO5
Assignment 1, equiv. to 1,000 words. Students solve problems using linear programming and simulation, and report on their findings.N/AN/AN/ANo15 SILO3, SILO5
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.N/AN/AN/ANo15 SILO5