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ISYS3402: Decision Analytics & Support Systems [Not available in 2019] (2020 - Semester 2)

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Unit: ISYS3402: Decision Analytics & Support Systems [not running in 2019] (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior
Faculty/School: School of Computer Science
Unit Coordinator/s: Dr Han, Caren
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: (ISYS2110 OR INFO2110) AND (ISYS2120 OR INFO2120).
Brief Handbook Description: With the rapid increases in the volume and variety of data available, the problem of providing effective support to facilitate good decision making has become more challenging. This unit of study will provide a comprehensive understanding the diverse types of decision and the decision making processes. It will introduce decision modelling and the design and implementation of application systems to support decision making in organisational contexts. It will include a range of business intelligence and analytics solutions based on online analytical processing (OLAP) models and technologies. The unit will also cover a number of modelling approaches (optimization, predictive, descriptive) and their integration in the context of enabling improved, data-driven decision making.
Assumed Knowledge: Database Management AND Systems Analysis and Modelling
Additional Notes: Students who wish to take ISYS3402 in 2019 should enrol in INFS3050 as a direct replacement unit. Further questions can be directed to Katie Yang or the UG Director Dr. Caren Han
Department Permission Department permission is required for enrollment in this session.
Lecturer/s: Dr Han, Caren
Timetable: ISYS3402 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 12
3 Project Work - own time 5.00 1 10
4 Independent Study 3.00
T&L Activities: Lectures, project work, lab-based data analyses and related work.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Unassigned Outcomes
1. Able to plan the project work, allocate work among the team members, and manage time and get the work done as part fo the group project
2. Communicate orally and in writing by completing the project work, writing up the report, and presenting a summary of what was accomplished in the project.
3. Able to independently research topics related to decision support and analytics systems and acquire new knowledge
4. Understand the fundamentals of decision making and related processes;

Ability ot model decisions and to map appropriate data and models to build systems to support tjhe decision,

Experience workign with a range of tools including online analytical processing tools such as COGNOS,

Learn the basics of data warehousing including ETL (extract, transform load),

Understand critical issues related to implementing descision support systems and dashboards.
5. Learn to gather requirements and to design applications to support decision making
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Group Project Yes 30.00 Week 12 1, 2, 3, 4, 5,
2 Final Examination No 50.00 Exam Period 3, 4,
3 Project presentation Yes 5.00 Week 12 2,
4 Quiz No 15.00 Week 6 3, 4,
Assessment Description: Group Project, Project presentation, Quiz, Final examination
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

See the policies page of the faculty website at for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar

Week Description
Week 1 Lecture/Tutorial: Introduction and inrto to decision making
Week 2 Lecture: The Data Component
Week 3 Lecture: Modelling component
Week 4 Lecture: Intelligence and Decision Support Systems.
Week 5 Lecture/Tutorial: The User Interface.
Week 6 Lecture/Tutorial: Data Warehousing and OLAP
Assessment Due: Quiz
Week 7 Lecture/Tutorial: Data Warehousing and OALP (continued)
Week 8 Lecture/Tutorial: UI and dashboards
Week 9 Lecture/Tutorial: Designing a Decision Support System
Week 10 Lecture/Tutorial: Implementation Strategy.
Week 11 Lecture/Tutorial: Group Decision Support Systems.
Week 12 Assessment Due: Group Project
Assessment Due: Project presentation
Week 13 Lecture: Presentations
Exam Period Assessment Due: Final Examination

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020, 2021
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020, 2021
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020, 2021
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020, 2021
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020, 2021
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020, 2021
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020, 2021
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020, 2021
Bachelor of Computer Science and Technology 2016, 2017
Bachelor of Computer Science and Technology (Advanced) 2016, 2017
Bachelor of Computer Science & Tech. Mid-Year 2016, 2017
Biomedical Mid-Year 2016, 2017, 2018, 2019, 2020
Biomedical 2016, 2017, 2018, 2019, 2020
Software Mid-Year 2021
Software/ Project Management 2021
Software 2021
Software / Arts 2021
Software / Commerce 2021
Software / Science 2021
Software/Science (Health) 2021
Software / Law 2021
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017
Software/Science (Medical Science Stream) 2021

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) No 0%
(7) Project and Team Skills (Level 4) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 2) No 0%
(4) Design (Level 2) No 0%
(3) Problem Solving and Inventiveness (Level 2) No 0%
(2) Engineering/ IT Specialisation (Level 4) No 0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.