The Master of Decision Analytics, (MDA), is designed for postgraduate scholars with an undergraduate qualification and/or extensive professional experience who wish to develop a high level understanding of the principles and practices of decision analytics and to strengthen their skills in this area.

The MDA program aims to allow students to develop a high level of understanding of the principles and practices of qualitative and quantitative decision making tools for analysing complex operations environments and making appropriate decisions within that environment.
On successful completion students will be able to:

1. Articulate a broad and complex body of knowledge about modelling tools for various types of complex decision problems;

2. Investigate the capability and limitations, and use of modelling tools and decision-making techniques to make complex decisions;

3. Critically analyse the decision-making process using quantitative data analysis and experimentation methodologies;

4. Use inquiry-based methodologies to synthesize complex information in formulating decision-making problems;

5. Evaluate the accuracy and effectiveness of solutions to decision-making problems using defined tools and processes;

6. Demonstrate awareness in projects of relevant and current issues as they relate to decision-making tools and processes; and

7. Demonstrate through projects, ethical, self-directed practice by synthesising decision-making problems from real-life situations, applying appropriate techniques to analyse the solutions, and appropriately interpreting the solutions for stake-holders.

Students undertaking the MDA are required to take 8 coursework courses (48 UOC) from the courses set out below . Students must complete four core courses (24 UOC) and four elective courses (24 UOC) from the list below.

Core Courses 24 UOC:

Students may, with the approval of the Postgraduate Coordinator, take up to two courses (12 UOC) from other coursework programs.

The option of undertaking a research project worth 12 UOC (ZEIT8271 Research Project - Decision Analytics F/T 12 UOC or two semesters of ZEIT8270 Research Project - Decision Analytics P/T 6 UOC) is available to Masters students who obtain a high credit average or better in four core courses and is subject to approval by the Program Authority. The project is recommended for those with a strong interest in pursuing original research in a particular area or intending to undertake a higher research degree qualification. Student undertaking the project are unable to enrol in courses from other coursework programs.

To gain entry into a Master of Decision Analytics program an applicant must have:

* Cognate discipline: Engineering, Business and Management, Humanities and Social Sciences, Mathematical Sciences and Information Technology

2.1 An application to be admitted as a candidate for the degree shall be made via Apply Online and enrolment via myUNSW or on the prescribed Enrolment Variation form and lodged with Student Administrative Services by the advertised date.
2.2 A candidate for the degree must undertake courses and pass assessment as prescribed by UNSW.
2.3 The program of study shall total a minimum of 48 units of credit.
2.4 The Academic Standing of a candidate shall be reviewed at the end of each session. Movement between levels of academic standing is based on progress, measured by cumulative number of failures.

Students who do not complete the full Master program requirements may exit the program with a Graduate Certificate in Operations Analysis (7634) qualification after completing a minimum 24 UOC. Of the four courses (24 UOC) students must have completed two core courses (ZEIT8402 Evidence-based Decision Making and ZEIT8404 Decision Making Analytics) plus two courses (core or elective).
For any advanced standing granted on entry into the Masters program, a maximum 12 UOC (for the elective course only) advanced standing can be applied to the Graduate Certificate.

For information regarding fees for UNSW programs, please refer to the following website:  UNSW Fee Website.