| AQF Level |
Level 9 Masters by Coursework Degree |
| CRICOS Code |
083779F |
| Overview and Aims of the Program |
This program is designed to train graduates for employment as applied statisticians in research organisations, insurance companies, financial institutions, medical institutions, government departments and industries. It includes specialised areas of study, such as biostatistics, data mining, epidemiological methods, generalised linear models, marketing research, time series and stochastic finance. The program has a strong focus on the application of contemporary statistical methods and the use the latest computational techniques. The development of relevant computing skills also forms an integral part of this program. The whole program can be completed internally, externally or a combination of the two modes. |
| 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:
KNOWLEDGE AND UNDERSTANDING
1. demonstrate a deep understanding of statistical theory and methods (K)
2. formulate statistical models, such as generalized linear models and commonly used models for multivariate analysis (K, T)
3. identify statistical methods for data from a broad range of statistical applications (K, T, P)
4. demonstrate an understanding of the ethical aspects and implications of professional statistical work (E, J).
SKILLS AND CAPABILITIES
5. apply appropriate statistical models/methods and relevant analyses for various types of data (K, T)
6. apply the latest computational techniques and modern statistical software packages in data analysis (K)
7. apply complex statistical analyses to address a wide range of practical problems (K, T, P, J)
8. interpret statistical results and report the results to a wide range of audience both verbally and in writing (C)
9. demonstrate the ability to undertake research project independently (K, T, P, C, E, J). |
| Learning and Teaching Methods |
In statistics, lectures and tutorials (or practicals) are 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 units of the Master of Applied Statistics. |
| Assessment |
Various assessment methods are used in the required units for the Master of Applied Statistics, which include problem solving, producing short or full statistical reports, oral presentations, active participation in lectures and/or tutorials, online quizzes, application of statistical methods using computers (which could be assessed within a computing lab under exam conditions or as a part of assignments) and a statistics project undertaken in STAT825.
The submissions of assessments could 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, formative assessments are used 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 and its associated Procedures and Guidelines. For recognition of prior informal and non-formal learning, please refer to the relevant RPL Plan, which describes the evidential requirements and approval processes for recognising prior learning for entry or credit in this program.
For undergraduate RPL plans visit www.goto.mq.edu.au/nonschoolrpl
For postgraduate RPL plans visit www.goto.mq.edu.au/pgrpl The Department of Statistics will only recognise formal learning for the purpose of granting credit towards the Master of Applied Statistics. However there might be exceptions where an individual student’s prior informal learning could also be considered for the purpose of granting credit towards the units required for the Master of Applied Statistics. |
| 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/health_and_wellbeing/contact_us |
| 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 career opportunities for graduates of this program include statisticians, statistical programmers, research assistants and statistical analysts in research organisations, insurance companies, financial institutions, medical institutions, government departments and industries. This program exposes students to and provides comprehensive knowledge and training in statistical applications in a range of specialised areas, such as biostatistics, data mining, marketing research and financial analysis, where applied statisticians have been in the past ten or more years and still are in demand. |
| 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. |
| Accreditation |
This is an Australian Qualifications Framework (AQF) accredited qualification.
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