| Overview and Aims of the Program |
Today’s organisations have access to many detailed sources of data, and complex decision-making strategies are required in order to operate effectively. Decision Science provides a theoretical framework and practical methods for translating data into efficiency improvements.
Students will learn how to manage resource allocation, supply chains, large projects, queues and inventory systems, how to study and predict markets, how to extract knowledge from large data sets using graphical and computational methods, and how to identify and work around uncertainty. |
| Graduate Capabilities |
The Graduate Capabilities Framework articulates the fundamentals that underpin all of Macquarie’s academic programs. It expresses these as follows:
Cognitive capabilities
(K) discipline specific knowledge and skills
(T) critical, analytical and integrative thinking
(P) problem solving and research capability
(I) creative and innovative
Interpersonal or social capabilities
(C) effective communication
(E) engaged and ethical local and global citizens
(A) socially and environmentally active and responsible
Personal capabilities
(J) capable of professional and personal judgement and initiative
(L) commitment to continuous learning |
| Program Learning Outcomes |
By the end of this program it is anticipated you should be able to:
1. allocate resources efficiently (for instance, determine the most profitable combination of products to manufacture using available raw materials) (K, T, P, A)
2. manage supply chains, from acquisition of raw materials, to scheduling of manufacturing, assembly, and packaging, to distribution (K, T, P)
3. plan and manage complex projects (in construction, business operations, system implementation and so forth) (K, T, P)
4. analyse and manage queue and inventory systems (K,T,P)
5. construct and use computer simulations of complex systems (K, T, P, I, J, L)
6. identify and work around uncertainty using modern statistical methods (K, T, P, J, L)
7. construct mathematical models representing business decisions (K, T, P, I, C, J)
8. communicate clearly, ethically and professionally with decision stakeholders (C, E, A, J, L)
9. apply theoretical decision science principles to interpret and generalise from calculation results (K, T, P, J, L). |
| Learning and Teaching Methods |
In Decision Science we mainly use lectures and tutorials (or practicals) as the main learning and teaching activities. Students are expected to spend extra time in self study to improve their understanding of the content of their units.
• Lectures: where usually the theory is introduced and if possible collaborative discussion and active learning exercises are used to improve student engagement with the content of the lectures.
• Tutorials: where usually theories are put into application either within computer laboratories or small tutorial groups. Students are encouraged to work together to enable peer learning.
• Practicals: similar to the tutorials where theories are put into application by the help of tutors/demonstrators. Students are encouraged to work together to enable peer learning.
• Self study: with extra learning materials, students are expected to enhance their learning in many of the Decision Science major units. |
| Assessment |
Various assessment methods are used in the required units for the Decision Science major, including problem solving, producing short or full reports, oral presentations, active participation in lectures and/or tutorials, online quizzes, application of technical methods using computers (which can be assessed within a computing lab under exam conditions or as a part of assignments).
The submissions of assessments can be individual or group work. Where group work is used, a self reflection and peer assessment/feedback in the form of contribution to the assessment task is incorporated into the requirements of the assessment so that individual contributions of each student can be identified.
As well as summative assessments, we use formative assessments to help students to learn without the fear of losing marks. These formative assessments are usually in the form of submitting tutorial/practical exercises weekly. |
| Recognition of Prior Learning |
Macquarie University may recognise prior formal, informal and non-formal learning for the purpose of granting credit towards, or admission into, a program. The recognition of these forms of learning is enabled by the University’s Recognition of Prior Learning (RPL) Policy (see www.mq.edu.au/policy) and its associated Procedures and Guidelines. The RPL pages contain information on how to apply, links to registers, and the approval processes for recognising prior learning for entry or credit.
Domestic Students
For undergraduate RPL information visit www.goto.mq.edu.au/nonschoolrpl
For domestic postgraduate RPL information visit www.goto.mq.edu.au/pgrpl
International Students
For RPL information visit www.mq.edu.au/international/rpl The Department of Statistics usually will recognise only formal prior learning when granting credit toward the Major in Decision Science, but may consider informal prior learning in exceptional cases. |
| Support for Learning |
Macquarie University aspires to be an inclusive and supportive community of learners where all students are given the opportunity to meet their academic and personal goals. The University offers a comprehensive range of free and accessible student support services which include academic advice, counselling and psychological services, advocacy services and welfare advice, careers and employment, disability services and academic skills workshops amongst others. There is also a bulk billing medical service located on campus.
Further information can be found at www.students.mq.edu.au/support/
Campus Wellbeing contact details:
Phone: +61 2 9850 7497
Email: campuswellbeing@mq.edu.au
www.students.mq.edu.au/support/wellbeing |
| Program Standards and Quality |
The program is subject to an ongoing comprehensive process of quality review in accordance with a pre-determined schedule that complies with the Higher Education Standards Framework. The review is overseen by Macquarie University's peak academic governance body, the Academic Senate and takes into account feedback received from students, staff and external stakeholders. |
| Graduate Destinations and Employability |
The knowledge gained in the Decision Science major is relevant to business management, engineering, finance, IT, inventory and supply chain management, logistics, manufacturing, project management, risk analysis and warehousing. Employment opportunities appear throughout government, defence, and the private sector.
Decision science is an extremely broad field, and graduates will go on to specialise in many areas, and require ongoing professional development. Various national professional associations support and register specialists in decision science fields. For instance, the Australian Institute of Project Management (est 1978; over 10000 members as of 2013) runs a certification program called Registered Project Manager (RegPM), stating, "Educational qualifications are a separate form of recognition from RegPM. RegPM is competency based and is not assessed by way of theory or knowledge of project management principles like educational institutions." With a major in Decision Science, Macquarie graduates are prepared for entry level positions in project management teams where they can start to develop the practical experience required for registration and advancement. Similar employment-based certification programs cover other areas of decision science, such as the Certified Professional Logistician. |
| Assessment Regulations |
This program is subject to Macquarie University regulations, including but not limited to those specified in the Assessment Policy, Academic Honesty Policy, the Final Examination Policy and relevant University Rules. For all approved University policies, procedures, guidelines and schedules visit www.mq.edu.au/policy. |
