Am I eligible?
Applicants must address the following criteria:
1. Academic qualifications considered:
- Bachelor degree
- Graduate diploma
- Graduate certificate
- Master’s degree
- Doctoral degree
2. The above qualifications may be in one of the following related disciplines:
- Mathematical Sciences
- Information Technology
- Physics and Astronomy
- Engineering and related technologies
- Banking, Finance and Related Fields
- Economics and Econometrics
If you have a degree in a different area, we still encouraged to you apply. Applicants with other academic qualifications may be considered on the basis of general and professional qualifications that demostrate their potential in the Master of Data Science and Innovation.
Do I need maths, programming, coding & stats?
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 would 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.
This test will help you determine your readiness for the 'statistical thinking for data science' subject, which is part of the Master of Data Science & Innovation course. At the end of the test, some useful reading and further resources are also provided for your reference.
What will I be taught?
Areas covered include the core data science skills such as statistics, machine learning and visualisation. In addition, the course 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 internal / external data challenges.
How much will it cost?
You can find out more about what your degree will cost at uts.edu.au/tuition-fee-calculator
For domestic students follow the steps below:
- Choose 'Search for fees by course'.
- fee type 'Postgraduate Domestic Coursework'
- fee year 2020
- cohort 2020
- course area ‘Transdisciplinary Innovation’
- course code ‘C04372’
To calculate the total cost of the course, multiply the 'Total CP' by the ‘Fee per CP'.
(Commonwealth supported places are not available for MDSI)
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).
How is the MDSI different from online, MOOCs or other Data Science degrees?
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.
What career opportunities are available to MDSI graduates?
Read all about the career options available to MDSI graduates on our Career Options page.
How long does it take?
For Autumn session intake, MDSI takes two-years to complete full-time or four-years, part-time.
For Spring session intake, MDSI takes two-and-a-half-years to complete full-time or four-to-five-years part-time (dependent on the number of subjects undertaken each session).
Can I study part-time?
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 is usually 36100 Data Science for Innovation, as it is offered in both Autumn and Spring and is a prerequisite for other compulsory subjects.
When are classes held?
Classes are usually held after 6.00pm 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.
What is the course structure?
Students must complete 96 credit points (CP), comprising 56CP core and 40CP optional subjects. Optional subjects can be selected from specified data science related optional subjects and from across the University’s disciplines. Enrolment in subjects from other disciplines is dependent on approval from the Course Director and subject coordinator, and usually requires demonstrated ability to meet pre-requisites. This flexible course structure enables students to pursue their own particular interests and career aspirations.
What is the teaching style?
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.
How do I apply?
Find out more about the application process and everything else you need to know on our How To Apply page.
Can I complete this course in a shorter amount of time?
To complete the Master of Data Science & Innovation it takes two-years, full-time or four-years, part-time (from Autumn semester intake). Data science is a complex field and requires diverse knowledge and skillsets to become a well-rounded data practitioner. Students will complete 56 credit points of core subjects (including 24 credit points of iLab projects) and 40 credit points of optional subjects.