Australian National University
Master of Applied Data Analytics
- Delivery: Face to Face
- Study Level: Postgraduate
- Duration: 18 months
- Course Type: Master's
Familiarise yourself with best practices in data analytics.
Course overview
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 practising data analytics professionals.
- The opportunity to undertake research of professional relevance.
The program is taught in semester mode and for domestic students, the program is also offered in intensive blended mode. Students studying in an intensive blended mode are expected to be enrolled part-time. The intensive blended course delivery mode is designed to suit working students who take leave from work (or other commitments) to attend an intensive one week of full-time learning on campus in the middle of the course and study remotely for the rest of the course. The intensive blended course delivery mode comprises: four weeks of online study, one full-time week of face-to-face learning on campus and a further four weeks of online study.
Key facts
26th July, 2027
What you will study
The Master of Applied Data Analytics requires the completion of 72 units, which must consist of:
48 units from completion of the following compulsory courses:
- Data Mining OR Data Mining (Intensive)
- Data Wrangling OR Data Wrangling (intensive)
- Introduction to Social Science Methods and Types of Data
- Using Data to Answer Policy Questions and Evaluate Policy
- Regression Modelling
- Generalised Linear Models
- Graphical Data Analysis
- Introductory Statistics for Business and Finance
Six units from completion of courses from the following list:
- Relational Databases
- Introduction to Database Concepts (intensive)
Six units from completion of courses from the following list:
- Programming for Scientists
- Introduction to Programming for Data Scientists (intensive)
12 units from completion of courses from any of the following lists:
Computer Science
- Statistical Machine Learning
- Computational Methods for Network Science
- Document Analysis OR Document Analysis (intensive)
- Neural Networks, Deep Learning and Bio-inspired Computing OR Neural Networks, Deep Learning and Bio-inspired Computing (intensive)
Social Science
- Social Research Practice
- Online Research Methods
- Advanced Techniques in the Creation of Social Science Data
- Advanced Social Science Approaches to Inform Policy Development and Service Delivery
Statistical Data Analysis
- Introduction to Bayesian Data Analysis
- Principles of Mathematical Statistics
- Statistical Learning
- Applied Time Series Analysis
Entry requirements
Applicants must present one of the following:
- Bachelor's degree with honours or international equivalent with a minimum GPA of 5.0/7.0.
- Bachelor's degree or international equivalent with a minimum GPA of 5.0/7.0, plus at least three years of relevant work experience.
The GPA for a Bachelor's program will be calculated from (i) a completed Bachelor's degree using all grades and/or (ii) a completed Bachelor's degree using all grades other than those from the last semester (or equivalent study period) of the Bachelor's degree. The higher of the two calculations will be used as the basis for admission.
Ranking and English Language Proficiency
All applicants must meet program-specific academic/non-academic requirements and English language requirements. Admission to most ANU programs is competitive, so meeting all admission requirements does not automatically guarantee entry.
In line with the University's admissions policy and strategic plan, an assessment for admission may include competitively ranking applicants based on specific academic achievement, English language proficiency and diversity factors. Applicants will first be ranked on a GPA ('GPA1') calculated using all but the last semester (or equivalent) of the Bachelor's degree used for admission purposes. If required, ranking may further be confirmed based on:
- GPA ('GPA2') calculated on the penultimate and antepenultimate semesters (or equivalent) of the Bachelor's degree used for admission purposes.
- And/or demonstrating higher-level English language proficiency.
Prior to enrolment in this ANU program, all students who gain entry will have their Bachelor's degree reassessed to confirm that the minimum requirements were met.
Contact the university or visit its website for more information.
Outcomes
Learning outcomes
- Select, adapt, apply and communicate advanced data analytics methods and techniques.
- Apply data analytics to policy, business and service delivery decision-making.
- 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.
- Communicate complex data analytics outcomes to diverse audiences.
Fees and FEE-HELP
Full-fee paying place
Indicative unit fee in 2027: $5,667.50 (6 ANU units per course)
Indicative annual fee in 2027: $45,340
Indicative full-course fee: $68,010
The average full-course fee shown is calculated based on the 72 ANU units (12 courses) course structure completed over 1.5 years.
Indicative annual fees are based on your first year of study.
A student’s annual fee may vary by:
- The number of units studied.
- Choice of units.
- Credit from previous study or work experience.
- Eligibility for government-funded loans.
Student fees shown are subject to change. Contact the university directly to confirm.
FEE-HELP loans are available to assist eligible full-fee-paying domestic students with the cost of a university course.