Warning! This degree has changed. Read the transition arrangements.
Warning! This is a restricted degree. Subject enrolment is only available to students admitted into this degree.
Master of Applied Statistics
Statistical analysis reveals behaviours in business and society
Develop advanced skills in analysing data using statistical software packages such as SPSS and SAS. Learn quantitative skills in estimation and hypothesis testing, factor and regression analysis, discriminant analysis and forecasting.
Australian Higher Education Loan Program (HELP)
Swinburne University of Technology leads the way with innovative and new ways of teaching, learning and thinking. It offers a wide range of study options, from pre-apprenticeships, undergraduate, postgraduate and PhDs, including online degrees with Open Universities Australia. Swinburne is known for career-oriented education and encouraging lifelong learning.
Times Higher Education Ranking
Graduates of the Master of Applied Statistics will be able to:
- Demonstrate advanced abilities to critically think, review, analyse and consolidate statistical knowledge to provide solutions to complex problems in diverse fields
- Synthesis communication skills to demonstrate an understanding of theoretical statistical concepts to transfer complex knowledge and ideas to a variety of audiences
- Defend the application of advanced statistical knowledge and skills to make high level, independent judgements in a range of technical or management functions in varied advanced contexts
- Demonstrate responsibility and accountability for personal outputs and all aspects of the work or function of others within broad parameters
- Demonstrate ability to perform advanced statistical modelling techniques using a variety of software
- Demonstrate ability to perform independent applied statistical research by working independently/collaboratively to collect, analyse, interpret and communicate the outcomes of research questions in diverse disciplines
- Demonstrate a high level of personal autonomy and accountability to work independently as a statistical consultant.
The demand for applied statisticians continues to grow as data production grows in all areas of the economy. Graduates are employed in areas such as the following:
- Government agencies
- Market research
- Medical and biological sciences
- Town planning
- Social research
- Medical Research.
The postgraduate Applied Statistics program aims to enable students to develop:
- Proficiency in statistical software packages such as SPSS and SAS
- An understanding of the need for, and methods of, acquiring good data
- Quantitative skills in areas such as exploratory data analysis, estimation and hypothesis testing, factor and regression analysis, discriminant analysis and forecasting
- An appreciation for the role of statistical analysis for revealing underlying relationships and behaviours in business and society
Applicants require either:
- An undergraduate degree and normally have an understanding of descriptive statistics; OR
- Successful completion of the Graduate Certificate of Applied Statistics and/or Graduate Diploma of Statistics with a credit average
English Proficiency Requirements
English language requirements: (International students only)
Swinburne College English for Academic Purposes Certificate: Swinburne English Language Centre: EAP 5 Advanced level with overall 70% and all skills 65% or above; OR, obtaining a minimum IELTS overall band of 6.5 (Academic Module) with no individual band below 6.0; OR, a TOEFL (Internet-based) minimum score 79 (Reading no less than 18, Writing no less than 20).
The media used to teach this course include printed materials and also some online study material. You will need easy and frequent access to a computer with the internet and email.
Additionally, students will be required to purchase and/or have access to the statistical software package SPSS.
SAS software required in Graduate Diploma and Master of Science subjects will be supplied by DVD. Please note: SAS will only run on a Windows platform.
Recognition of Prior Learning (RPL) is a process where a student may be granted credit or partial credit towards a qualification in recognition of skills and knowledge gained through work experience, life experience and/or formal training. For further details for students considering Higher Education degrees visit the RPL website: http://www.future.swinburne.edu.au/pathways/workforce/index.html
Applicants with prior tertiary studies that satisfy part of the academic requirements of this degree may be granted ‘credit’ and/or entry into the degree with ‘advanced standing’. University policies apply and applicants are assessed on a case-by-case basis. For further information please refer to http://www.swinburne.edu.au/open-universities-australia/advanced-standing-rpl.html
Please note: The degree has undergone an award title change from the Master of Science (Applied Statistics) to the Master of Applied Statistics.
The Master of Applied Statistics is degree is designed for graduates in any discipline, especially for humanities, social sciences, health sciences, information systems, business and science graduates who have a professional interest in the use of statistics. It is also applicable to other graduates who have a need to use advanced level of statistics in their work but have not had sufficient or current advanced training in applied statistics. It concentrates on advanced practical skills but also provides advanced theoretical and practical knowledge in multiple areas of statistics, while developing research skills and experience.
The postgraduate Applied Statistics program includes degrees from the Graduate Certificate, Graduate Diploma and Master levels. In particular it builds proficiency in the SAS software package as well as SPSS. It provides students with experience in analysing multivariate data sets and in the analysis of complex sample data.
Recommended Study Pattern
To qualify for the Master of Applied Statistics, a student must complete all sixteen core subjects. A subject can only be counted once. The program is delivered entirely online and is supported with eTutors, tutorials, print materials, websites and CD-ROMs.
Each subject is worth 12.5 credit points so a total of 200 credit points (16 subjects) must be studied to complete the Master of Applied Statistics qualification. In each year, eight subjects normally constitute a full-time load of 100 credit points and four subjects normally constitute a part-time load of 50 credit points.
To qualify for the award of Master of Applied Statistics, a student must complete 16 core subjects of study (200cp) enrolling on a full-time or part-time basis.
Please note: You cannot re-enrol into Swinburne subjects that you have already passed.
Taking time off from study (Absent Without Leave and Leave of Absence): How to apply for an approved leave of absence and minimise the risk of having to reapply for degree admission: https://www.swinburne.edu.au/current-students/manage-course/enrolment-timetable/time-off/
The Master of Applied Statistics has two early exit points:
The Graduate Certificate of Applied Statistics (4 subjects) and Graduate Diploma of Applied Statistics (8 subjects).
From Study Period 1 2018, the Master of Science (Applied Statistics) changed to the Master of Applied Statistics. The Course Structure remains the same with some minor changes to subjects.
Master of Applied Statistics and nested courses including the Graduate Certificate and Graduate Diploma
Students may choose to stay in the previous Course title or transfer to the new Course title. Students who wish to transfer to the new Course title must complete the online Course Transition Application.
For all students
The following subject changes apply from 2018:
- STA60003 Basic Statistical Computing changed to STA60003 Basic Statistical Computing using R
- STA80005 Statistical Marketing Tools changed to STA80005 Advanced Data Mining
The following subject changes apply from 2019:
- STA80006 Using R for Statistical Analysis changed to STA80006 Statistical Decision Making
- STA80004 Advanced Topics in Regression replaced by STA80011 Advanced Statistical Modelling