Master of Data Analytics

2022 Deakin University Handbook

Note: You are seeing the 2022 view of this course information. These details may no longer be current. [Go to the current version]
Year

2022 course information

Award granted Master of Data Analytics
Course Map

Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central.

Campus

For students who commenced prior to 2019

Duration

1.5-2 years full-time or part-time equivalent depending on your entry point

CRICOS course code089186E
Deakin course codeS777
Approval status

This course is approved by the University under the Higher Education Standards Framework.

Australian Qualifications Framework (AQF) recognition

The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9.

The final intake to this course version was in 2018.

Students should contact a Student Adviser in Student Central for course and enrolment information.

Further course structure information can be found in the Handbook archive.

Course sub-headings

Course overview

Deakin’s Master of Data Analytics prepares students for professional employment across all sectors.  The sheer volume and complexity of data already at the fingertips of businesses and research organisations gives rise to challenges that must be solved by tomorrow’s graduates.  Become a data analytics specialist capable of using data to learn insights and support decision making.

Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions.

Throughout your studies you’ll learn to understand the various origins of data to be used for analysis, combined with methods to manage, organise and manipulate data within regulatory, ethical and security constraints. You’ll develop specialised skills in categorising and transferring raw data into meaningful information for the benefit of prediction and robust decision-making.

As a graduate, your knowledge, skills and competencies in modern data science and statistical analysis will be highly valued by employers seeking greater efficiencies and competitive advantage through data insights.

Units in the course may include assessment hurdle requirements.

Indicative student workload

You can expect to participate in a range of teaching activities each week. This could include classes, seminars, practicals and online interaction. You can refer to the individual unit details in the course structure for more information. You will also need to study and complete assessment tasks in your own time.

Career opportunities

Graduates of this course may find careers as data analysts, data scientists, analytics programmers, analytics managers, analytics consultants, business analysts, management advisors, management analysts, business advisors and strategists, marketing managers, market research analysts and marketing specialists.

Participation requirements

Elective units may be selected that include compulsory placements, work-based training, community-based learning or collaborative research training arrangements.

Reasonable adjustments to participation and other course requirements will be made for students with a disability. Click here for more information.

Mandatory student checks

Any unit which contains work integrated learning, a community placement or interaction with the community may require a police check, Working with Children Check or other check.

Alternative exits

Graduate Certificate of Data Analytics (S576)
Graduate Diploma of Data Science (S677)

Fees and charges

Fees and charges vary depending on your course, the type of fee place you hold, your commencement year, the units you choose and your study load. To find out about the fees and charges that apply to you, visit the Current students fees website or our handy Fee estimator to help estimate your tuition fees.

Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as photocopying or travel.

Course rules

To complete the Master of Data Analytics, students must attain 16 credit points. Most units (think of units as ‘subjects’) are equal to 1 credit point. So that means in order to gain 16 credit points, you’ll need to study 16 units (AKA ‘subjects’) over your entire degree. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.

The course comprises a total of 16 credit points, which must include the following:

  • Four (4) core foundation units (MIS770, MIS782, SIT772, SIT718)*
  • Four (4) core analytics units (MIS771, MIS772, SIT719, SIT720)
  • Four (4) ‘Data’ discipline specific units (SIT741, SIT742, SIT743, SIT744)
  • Four (4) credit points of core project/thesis/placement units from the list provided
  • Completion of STP050 Academic Integrity (0-credit-point compulsory unit)

* Core foundation units for students entering with a degree from a different discipline. Students who are entering with a related academic/professional background may be eligible for credit transfer and recognition for up to 4 of the foundation units.  Please contact your course advisor for more detailed information.

Students are required to meet the University's academic progress and conduct requirements. Click here for more information.

Work experience

You will have an opportunity to undertake a placement as part of your course.


Other course information

Course duration - additional information

Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.

Further information

Student Central can help you with course planning, choosing the right units and explaining course rules and requirements.

Other learning experiences

You may choose to use one of your elective units to undertake an internship or participate in an overseas study tour to enhance your global awareness and experience.