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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.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Andrew Skabar

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 5 - Masters

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: N/A

Co-requisites: N/A

Incompatible subjects: CSE4DSS

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Business Intelligence and Analytics: Systems for Decision Support

Resource Type: Book

Resource Requirement: Prescribed

Author: Ramesh Sharda Dursun Delen and Efraim Turban

Year: 2014

Edition/Volume: N/A

Publisher: Pearson

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/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

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Subject Instance Co-ordinator: Andrew Skabar

Class requirements

Computer LaboratoryWeek: 11 - 22
One 2.00 hours 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 hour 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/ANo70SILO1, 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/ANo15SILO3, 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/ANo15SILO5