single degree

Master of Applied Data Analytics

A single 1.5 year graduate degree offered by the ANU College of Engineering and Computer Science

MADATAN
  • Length 1.5 year full-time
  • Minimum 72 Units
First year student? There’s more information about enrolling in your degree.
  • Mode of delivery
    • In Person
  • Field of Education
    • Information Technology
  • Academic contact
  • Length 1.5 year full-time
  • Minimum 72 Units
First year student? There’s more information about enrolling in your degree.
  • Mode of delivery
    • In Person
  • Field of Education
    • Information Technology
  • Academic contact

Program Requirements

The Master of Applied Data Analytics requires the completion of 72 units, which must consist of:



The core components of the program are offered in two teaching modes: semester mode and blended intensive mode. The intensive mode has been designed to cater to applied data analytics students who are working while studying, for more information see the Program Overview. Some elective courses are also offered in blended intensive mode.


48 units from completion of the following compulsory courses:

COMP8410 Data Mining or COMP8910 Data Mining (Intensive)

COMP8430 Data Wrangling or COMP8930 Data Wrangling (intensive)

SOCR8201 Introduction to Social Science Methods and Types of Data

SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy

STAT6038 Regression Modelling

STAT6030 Generalised Linear Models

STAT6026 Graphical Data Analysis

STAT7055 Introductory Statistics for Business and Finance

6 units from completion of courses from the following list:

COMP6240 Relational Databases

COMP7240 Introduction to Database Concepts (intensive)

6 units from completion of courses from the following list:

COMP6730 Programming for Scientists

COMP7230 Introduction to Programming for Data Scientists (intensive)

 12 units from completion of courses from any of the following lists:

Computer Science

COMP8600 Statistical Machine Learning

COMP8880 Computational Methods for Network Science

COMP6490 Document Analysis or COMP6990 Document Analysis (intensive)

COMP8420 Neural Networks, Deep Learning and Bio-inspired Computing or COMP8920 Neural Networks, Deep Learning and Bio-inspired Computing (intensive)

Social Science

SOCR8082 Social Research Practice

SOCR8006 Online Research Methods

SOCR8203 Advanced Techniques in the Creation of Social Science Data

SOCR8204 Advanced Social Science Approaches to Inform Policy Development and Service Delivery

Statistical Data Analysis

STAT7016 Introduction to Bayesian Data Analysis

STAT6039 Principles of Mathematical Statistics

STAT7040 Statistical Learning

STAT8002 Applied Time Series Analysis

Study Options

Year 1 48 units COMP6730 Programming for Scientists 6 units COMP6240 Relational Databases 6 units STAT7055 Introductory Statistics for Business and Finance 6 units SOCR8202 Using Data to Answer Policy Questions and Evaluate Policy 6 units
COMP8430 Data Wrangling 6 units STAT6039 Principles of Mathematical Statistics 6 units SOCR8201 Introduction to Social Science Methods and Types of Data 6 units STAT7026
Year 2 COMP8410 Data Mining 6 units STAT6038 Regression Modelling 6 units Stage 3 Elective Course 6 Units Stage 3 Elective Course 6 units

Admission Requirements

Applicants must present one of the following:

  • A Bachelor degree with Honours or international equivalent with a minimum GPA of 5.0/7.0
  • A Bachelor degree or international equivalent with a minimum GPA of 5.0/7.0, plus at least 3 years of relevant work experience

The GPA for a Bachelor program will be calculated from (i) a completed Bachelor degree using all grades and/or (ii) a completed Bachelor degree using all grades other than those from the last semester (or equivalent study period) of the Bachelor degree. The higher of the two calculations will be used as the basis for admission.

Ranking and English Language Proficiency: At a minimum, all applicants must meet program-specific academic/non-academic requirements, and English language requirements. Admission to most ANU programs is on a competitive basis. Therefore, 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 on the basis of specific academic achievement, English language proficiency and diversity factors. Applicants will first be ranked on a GPA ('GPA1') that is calculated using all but the last semester (or equivalent) of the Bachelor degree used for admission purposes. If required, ranking may further be confirmed on the basis of:

  • a GPA ('GPA2') calculated on the penultimate and antepenultimate semesters (or equivalent) of the Bachelor 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 degree reassessed, to confirm minimum requirements were met.

Further information: English language admission requirements and post-admission support

Diversity factors: As Australia‚Äôs national university, ANU is global representative of Australian research and education. ANU endeavours to recruit and maintain a diverse and deliberate student cohort representative not only of Australia, but the world. In order to achieve these outcomes, competitive ranking of applicants may be adjusted to ensure access to ANU is a reality for brilliant students from countries across the globe.

Assessment of qualifications: Unless otherwise indicated, ANU will accept all Australian Qualifications Framework (AQF) qualifications or international equivalents that meet or exceed the published admission requirements of our programs, provided all other admission requirements are also met.

Where an applicant has more than one completed tertiary qualification, ANU will base assessment on the qualification that best meets the admission requirements for the program. Find out more about the Australian Qualifications Framework: www.aqf.edu.au.

Unless otherwise indicated, where an applicant has more than one completed tertiary qualification, ANU will calculate the GPA for each qualification separately. ANU will base assessment on the best GPA of all completed tertiary qualifications of the same level or higher.

ANU uses a 7-point Grade Point Average (GPA) scale. All qualifications submitted for admission at ANU will be converted to this common scale, which will determine if an applicant meets our published admission requirements. Find out more about how a 7-point GPA is calculated for Australian universities: www.uac.edu.au/future-applicants/admission-criteria/tertiary-qualifications.

Application for course credits: Applicants who have completed a degree in a cognate discipline from a recognised university may be eligible to receive coursework credit towards this degree, in line with the ANU Coursework Award Rules.

Cognate Disciplines

Actuarial Studies, Anthropology, Computer Science, Criminology, Demography/Population Studies, Engineering, Epidemiology/Public Health, Finance, Information Technology, Maths, Physics, Political Science, Psychology, Sociology, Statistics.

Domestic Tuition Fees (DTF)

For more information see: http://www.anu.edu.au/students/program-administration/costs-fees

Annual indicative fee for international students
$49,330.00

For further information on International Tuition Fees see: https://www.anu.edu.au/students/program-administration/fees-payments/international-tuition-fees

Fee Information

All students are required to pay the Services and amenities fee (SA Fee)

The annual indicative fee provides an estimate of the program tuition fees for international students and domestic students (where applicable). The annual indicative fee for a program is based on the standard full-time enrolment load of 48 units per year (unless the program duration is less than 48 units). Fees for courses vary by discipline meaning that the fees for a program can vary depending on the courses selected. Course fees are reviewed on an annual basis and typically will increase from year to year. The tuition fees payable are dependent on the year of commencement and the courses selected and are subject to increase during the period of study.

For further information on Fees and Payment please see: https://www.anu.edu.au/students/program-administration/fees-payments

Scholarships

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 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 is taught in semester mode, and for domestic students the program is also offered in intensive blended mode. Students studying in 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 1 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: 4 weeks of online study, 1 full time week of face to face learning on campus, followed by a further 4 weeks of online study.

Career Options

ANU ranks among the world's very finest universities. Our nearly 100,000 alumni include political, business, government, and academic leaders around the world.

We have graduated remarkable people from every part of our continent, our region and all walks of life.

Learning Outcomes

  1. Select, adapt, apply, and communicate advanced data analytics methods and techniques;
  2. Apply data analytics to decision making about policy, business and service delivery;
  3. Examine current issues in data analytics using leading-edge research and practices in the field;
  4. 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
  5. Communicate complex data analytics outcomes to diverse audiences.

Inherent Requirements

Information on inherent requirements is currently not available for this program.

Academic Advice

Important things to keep in mind when planning your enrolment

The following links enable students to download a recommended enrolment pattern for their individual situation: domestic or international; online or on-campus.

Students should note that these patterns are strictly advisory. They are designed to help students navigate the degree by taking account of factors such as pre-requisites (what order students have to take some courses in) and scheduling constraints (some courses occur more frequently than others). Students are free to vary these patterns, but the College cannot guarantee that a student who diverges from the recommended pattern will be able to complete the program in the minimum time.


Please consult the  CECS MADA page for detailed info about fees, tuition sponsorships, courses, FAQ etc   https://cecs.anu.edu.au/study/dataanalytics

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