- Length 1.5 year full-time
- Minimum 72 Units
- Academic plan MADAN
- CRICOS code NO CRICOS
- UAC code
Field of Education
- Mathematical Sciences not else
The Master of Applied Data Analytics is a 1.5 year full-time (or equivalent part-time) degree that provides students with:
- Exposure to best practice in data analytics.
- Cutting edge courses in areas of relevance to data analytics practitioners.
- An opportunity to deepen knowledge in one of the three areas of computation, statistics, or social science.
- Professional development for practicing data analytics professionals.
- The opportunity to undertake research of professional relevance.
The program will be taught in intensive blended mode with students expected to be enrolled part-time.
Graduates from ANU have been rated as Australia's most employable graduates and among the most sought after by employers worldwide.
The latest Global Employability University Ranking, published by the Times Higher Education, rated ANU as Australia's top university for getting a job for the fourth year in a row.
Upon successful completion, students will have the skills and knowledge to:
- Select, adapt, apply, and communicate advanced data analytics methods and techniques;
- Apply data analytics to decision making about policy, business and service delivery;
- Examine current issues in data analytics using leading-edge research and practices in the field;
- Demonstrate strong cognitive, technical, and communication skills to work independently and collaboratively to collect, process, interpret and communicate the outcomes of data analytics problems; and
- Communicate complex data analytics outcomes to diverse audiences.
Please provide the following when you apply:
- Graduation certificates (testamurs) and academic transcripts for all formal study
Students will need to install certain free software packages in order to successfully complete COMP7230. Please see the ‘Additional Information’ section on course information page.
A completed Bachelor with Honours degree or equivalent in any discipline from a recognised university; OR
A completed Bachelor degree or international equivalent + 3 years of relevant work experience.
You must be one of the following:
- an Australian citizen
- an New Zealand citizen (or dual citizenship holders of either Australia or New Zealand)
- an Australian permanent resident
- an Australian humanitarian visa holder
Applicants who have completed a degree in a cognate discipline may be eligible to receive credit in line with the ANU Graduate Coursework Award Rules towards their Master of Applied Data Analytics degree.
Actuarial Studies, Anthropology, Computer Science, Criminology, Demography/Population Studies, Engineering, Epidemiology/Public Health, Finance, Information Technology, Maths, Physics, Political Science, Psychology, Sociology, Statistics.
- Annual indicative fee for domestic students
For more information see: http://www.anu.edu.au/students/program-administration/costs-fees
ANU offers a wide range of scholarships to students to assist with the cost of their studies.
Eligibility to apply for ANU scholarships varies depending on the specifics of the scholarship and can be categorised by the type of student you are. Specific scholarship application process information is included in the relevant scholarship listing.
For further information see the Scholarships website.
The Master of Applied Data Analytics requires the completion of 72 units, which must consist of:
60 units from the following compulsory courses:
COMP7230 Introduction to Programming for Data Scientists
COMP7240 Introduction to Database Concepts
COMP8410 Data Mining
COMP8430 Data Wrangling
SOCR8201 Introduction to social science methods and types of data
SOCR8202 Using data to answer policy questions and evaluate policy
STAT7055 Introductory Statistics for Business and Finance
STAT7001 Applied Statistics
STAT6039 Principles of Mathematical Statistics
STAT7026 Graphical Data Analysis
12 units from completion of courses from any of the following lists:
COMP8600 Introduction to Statistical Machine Learning
COMP6490 Document Analysis
COMP8420 Bio-inspired Computing: Applications and Interfaces
SOCR8203 Advanced techniques in the creation of social science data
SOCR8204 Advanced social science approaches to inform policy development and service delivery
Statistical Data Analytics
STAT7040 Statistical Learning
STAT7016 Introduction to Bayesian Data Analysis
STAT7017 Big Data Statistics