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Master of Applied Statistics

APST12MV3

Faculty:
Faculty of Science and Engineering
Award:
Master of Applied Statistics (MAppStat)
Admission Requirement:
• Australian level 7 bachelor's qualification or recognised equivalent
• GPA of 4.50 (out of 7.00)
English Language Proficiency:
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: 1 year - 2 years 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 2 years
General requirements:
Minimum number of credit points 64
Minimum number of credit points at 600 level 16
Minimum number of credit points at 800 level or above 48
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
4
Statistical Theory (4)
 
Required
4
Generalized Linear Models (4)
 
Required
4
Multivariate Analysis (4)
 
Required
4
Statistics Project (4)
 
Required
4
Modern Computational Statistical Methods (4)
 
Required
20cp from
 
Statistical Design (4)
 
 
Epidemiological Methods (4)
 
 
Time Series (4)
 
 
Statistical Graphics (4)
 
 
Market Research and Forecasting (4)
 
 
Survival Analysis (4)
 
 
Data Mining (4)
 
20
Stochastic Finance (4)
 
Required
8cp from
 
Mathematical Theory of Risk (4)
 
 
Data Analytics for Finance and Insurance (4)
 
 
Data Analytics Tools for Finance and Insurance (4)
 
 
Health Indicators and Health Surveys (4)
 
 
Clinical Biostatistics (4)
 
 
Linear Models (4)
 
 
Bioinformatics (4)
 
 
Longitudinal and Correlated Data (4)
 
 
Bayesian Statistical Methods (4)
 
 
Statistical Design (4)
 
 
Epidemiological Methods (4)
 
 
Time Series (4)
 
 
Statistical Graphics (4)
 
 
Market Research and Forecasting (4)
 
 
Survival Analysis (4)
 
 
Data Mining (4)
 
8
Stochastic Finance (4)
 

TOTAL CREDIT POINTS REQUIRED FOR THIS PROGRAM

64
Program Learning Outcomes and Additional Information
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, o