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
  • Field of Education
    • Mathematical Sciences not else
  • Length 1.5 year full-time
  • Minimum 72 Units
  • 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.

Career Options

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.

<p>This program is available for applications to commence from</p> Autumn Session, 2016

Learning Outcomes

Upon successful completion, students will have the skills and knowledge to:

  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.

Further Information

Please provide the following when you apply:

  • CV
  • 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.

Admission Requirements

Either:

A completed AQF8 degree or equivalent in any discipline from a recognised university; OR

Or:

A completed AQF7 degree + 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.

 

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.

Annual indicative fee for domestic students
$27,840.00

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

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.

Program Requirements

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:

Data Science

COMP8600 Introduction to Statistical Machine Learning

COMP6490 Document Analysis

COMP8420 Bio-inspired Computing: Applications and Interfaces

 

Social Science

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

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