Skip to Content


Graduate Diploma of Applied Statistics

APST19DPV1

Faculty:
Faculty of Science and Engineering
Award:
Graduate Diploma of Applied Statistics (GradDipAppStat)
Admission Requirement:
• Australian level 7 bachelor's qualification or recognised equivalent
• GPA of 4.00 (out of 7.00) or overseas equivanet
English Language Proficiency:
Academic IELTS of 6.5 overall with minimum 6.0 in each band, or equivalent
Study Mode:
Full-time, Part-time
Attendance Mode:
Internal, External
Candidature Length:
Full-time: 0.5 years - 1 year depending on RPL granted
Commencement:
North Ryde — Session 1 (February)
North Ryde — Session 2 (July)
External — Session 1 (February)
External — Session 2 (July)
Volume of Learning:
Equivalent to 1 year
General requirements:
Minimum number of credit points 32
Minimum number of credit points at 600 level 16
Minimum number of credit points at 800 level or above 16
Completion of other specific minimum requirements as set out below

In order to graduate students must ensure that they have satisfied all of the general requirements of the award.

Specific minimum requirements:

Credit points

600 level

Required
4
Mathematical Modelling (4)
 
Required
4
Introductory Statistics (4)
 
Required
4
Applied Statistics (4)
 
Required
4
Introduction to Probability (4)
 

800 level

Required
16cp from
 
Statistical Theory (4)
 
 
Generalized Linear Models (4)
 
 
Statistical Design (4)
 
 
Epidemiological Methods (4)
 
 
Multivariate Analysis (4)
 
 
Time Series (4)
 
 
Statistical Graphics (4)
 
 
Market Research and Forecasting (4)
 
 
Survival Analysis (4)
 
 
Data Mining (4)
 
 
Modern Computational Statistical Methods (4)
 
16
Stochastic Finance (4)
 

TOTAL CREDIT POINTS REQUIRED FOR THIS PROGRAM

32
AQF Level Level 8 Graduate Diploma
CRICOS Code 083759K
Overview and Aims of the Program This program is designed to train graduates for employment as applied statisticians in research organisations, 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 emphasis of the program is on the application of statistical methods, using computational techniques and statistical software packages.

This program is catered not only for people who completed an undergraduate study in statistics or related field, but also those from other areas of study, who wish to learn more statistics, particularly its applications.
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 methods (K)
2. formulate statistical models (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 for various types of data (K, T)
6. select and use suitable modern statistical software packages for statistical analyses (K)
7. appy complex statistical analyses to address a wide range of practical problems (K, T, P, J)
8. interpret statistical results to a wide range of audience (C).
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 Graduate Diploma of Applied Statistics.
Assessment Various assessment methods are used in the required units for the Graduate Diploma 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 on statistical methods using computers (which could be assessed within a computing lab under exam conditions or as a part of assignments).

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 (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.


Information can be found at: https://mq.edu.au/rpl

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 career opportunities for graduates of this program include statisticians, statistical programmers, research assistants and statistical analysts in research organisations, 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.

Inherent requirements are the essential components of a course or program necessary for a student to successfully achieve the core learning outcomes of a course or program. Students must meet the inherent requirements to complete their Macquarie University course or program.

Inherent requirements for Macquarie University programs fall under the following categories:

Physical: The physical inherent requirement is to have the physical capabilities to safely and effectively perform the activities necessary to undertake the learning activities and achieve the learning outcomes of an award.

Cognition: The inherent requirement for cognition is possessing the intellectual, conceptual, integrative and quantitative capabilities to undertake the learning activities and achieve the learning outcomes of an award.

Communication: The inherent requirement for communication is the capacity to communicate information, thoughts and ideas through a variety of mediums and with a range of audiences.

Behavioural: The behavioural inherent requirement is the capacity to sustain appropriate behaviour over the duration of units of study to engage in activities necessary to undertake the learning activities and achieve the learning outcomes of an award.

For more information see https://students.mq.edu.au/study/my-study-program/inherent-requirements



2019 Unit Information

When offered:
S1 Day
Prerequisites:
Permission of Executive Dean of Faculty
Corequisites:
None
NCCWs:
HSC Chinese, CHN113, CHN148