FAQs

To be eligible for the Master of Data Science and Innovation, applicants must have completed a UTS-recognised bachelors degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.

The bachelor degree or higher qualification should be in a relevant discipline, such as mathematical sciences, computer science, physics and astronomy, engineering, accounting, banking, finance and related fields or economics and econometrics.

If your qualification is not in a relevant discipline, you must provide detailed evidence in your CV of prior learning and/or work experience, demonstrating your knowledge with basic quantitative data skills, key mathematical concepts and programming experience.

Applicants with other qualifications may be considered on the basis of general and professional qualifications that demonstrate their potential in the Master of Data Science and Innovation.

For full admission requirements, check the admission requirements in the additional information section of the Master of Data Science and Innovation course page.

Basic stats and quantitative skills (comfortable using spreadsheets, for example) are treated as assumed knowledge for the course. If you feel that your quantitative stats skills aren’t up to speed, we recommend taking a bridging course or using one of your electives to do a stats subject in your first semester.

Knowledge of a programming language would be an advantage but is not essential.

Areas covered include the core data science skills such as statistics, machine learning and visualisation.

The course also includes a number of non-traditional areas covering the social and ethical aspects of the profession, as well as the creative elements of data science such as dealing with ambiguity and problem formulation.

An important aspect of the course is that students are given many opportunities to work on real data challenges through their iLab projects and subject assessments.

You can find out more about what course fees by using the UTS course fee calculator tool for domestic students.

To use the calculator, choose:

• 'search for fees by course'.
• fee type 'Postgraduate Domestic Coursework'
• fee year 2021
• cohort 2021
• course area ‘Transdisciplinary Innovation’
• course code ‘C04372’

To calculate the total cost of the course, multiply the 'Total CP' by the ‘Fee per CP'.

Note: Commonwealth supported places are not available for MDSI.

Already completed a UTS degree?

If you've already completed a degree at UTS, then you're eligible for the Alumni Advantage program, which offers a 10 per cent savings on full fee-paying degree programs.

Find out if you're eligible at Alumni Advantage.

If you do have to pay a fee and you’re a local student, you may be eligible for FEE-HELP, an Australian Government loan scheme. Using FEE-HELP means you don’t have to pay for your tuition fees up front. More information can be found at uts.edu.au/government-help-schemes.

You can choose to repay your FEE-HELP loan simply by notifying your employer who will then withhold your payments through the PAYG tax system. You can also make payments directly to the Australian Taxation Office (ATO).

If you’re an international student, head to uts.edu.au/international to find the course information, fees and application details relevant to you.

The MDSI is unique in its approach and feel. It’s a postgraduate degree specifically designed to allow you to make professional connections with industry partners and hone your data skills with our experienced academic team. Our point of difference is our commitment to innovation, creative problem solving and ethically minded data science practice.  In a world of explosive data potential, the ability to think and work systematically, ethically and creatively is highly valued by employers.

Data science is a collaborative discipline. Students in the MDSI program get hands-on experience working in teams to solve real-life data science problems. The MDSI program is structured in a way that helps students learn this crucial skill. 

Another important aspect of data science is that it is a rapidly evolving field. A data scientist must therefore be able to stay current with developments in the field. The MDSI program, with its emphasis on critical self-learning, prepares students to be lifelong learners.

Read all about the career options available to MDSI graduates on our career options page.

MDSI can be as flexible as you make it. You can choose how fast you go through the degree based on the number of subjects you wish to take on in any given semester and you have 5.5 years to finish your degree. The only requirement is that you must be doing at least one subject in your first semester.

This course is offered on a two-year, full-time or four-year, part-time basis — dependent on the number of subjects undertaken each session — with intakes in both Autumn and Spring session each year.

Classes are usually held after 6.00pm on weekdays and all-day Saturdays. They’re also not held every week, so a typical subject may have three evenings (6-9pm) and two Saturday session (9am-5pm) over the semester. A full-time load is three subjects, so it is possible to schedule class around full-time work. What students usually find the most challenging is finding time for assessments and other pre-class work, which is often why students elect to study part-time.

To understand what your study schedule may look like, check out the UTS class timetable builder. You will find a list of the course subjects in the UTS Handbook under course program.

Student must complete 96 credit points (cp), comprising 44cp core subjects, 32cp specified data science related optional subjects and 20cp elective subjects. Elective subjects can be selected from data science related subjects and from across the University’s disciplines. Enrolment in subjects from other disciplines is dependent on approval from the subject coordinator of the host faculty, and usually requires demonstrated ability to meet pre-requisites. This flexible course structure enables students to pursue their own particular interests and career aspirations.

For more detailed information on the course structure and each of the subjects, visit our UTS Handbook.

96cp = 44cp core subjects + 32cp optional subjects + 20cp elective subjects

MDSI is delivered via a blended mode, which means there are classes held on campus, where students get the opportunity to network and learn from renowned academics. Students are also expected to use online content outside of class to carry on working both independently and collaboratively.

Find out more about the application process and everything else you need to know on our how to apply page.

To complete the Master of Data Science and Innovation, it takes two-years, full-time or four year, part-time. Data science is a complex field and requires diverse knowledge and skillsets to become a well-rounded data practitioner. Students will complete 96 credit points (cp), comprising 44cp core subjects, 32cp specified data science related optional subjects and 20cp elective subjects.

However, if you are not quite ready to commit to the full masters, we do offer a range of flexible study options:

• Graduate Certificate in Data Science and Innovation
• Graduate Diploma in Data Science and Innovation, and
• two new microcredentials:
  • Applied Data Science for Innovation
  • Advanced Data Science for Innovation

Successful completion of our microcredentials can count as 4cp towards the MDSI.

A maximum of 32cp exemptions may be granted for the course with a maximum of 12cp being unspecified subjects. Exemptions are granted only on the basis of prior postgraduate study at an Australian university, or at a recognised overseas institution deemed to be equivalent to an Australian university.

To be eligible for recognition of prior learning, the subject being considered for prior study must have been completed within five years of commencing the course. Recognition of study completed before this period is not considered.

To apply for recognition of prior learning, follow the steps on how to apply for UTS recognition of prior learning.