Our rankings
Ranked 65th globally for engineering and technology by the 2021 QS University Rankings by Subject
Our graduates are ranked 1st in Australia and 4th globally for employability by the 2022 QS Graduate Employability Rankings
In response to COVID-19, the University of Sydney has reviewed the availability of courses to be delivered remotely.
This course will be available for students to study remotely during 2022 if they are unable to undertake face-to-face study. Please note that some units of study that are not essential to completing the degree may not be available to be studied remotely. For the latest updates on course availability, visit the COVID-19 information page.
About this course
If you are a graduate with qualifications in qualitative areas such as health and education, the Graduate Certificate in Data Science will provide you with the data science capability to complement your existing skills and provide a pathway to the Master of Data Science.
Data is a vital asset to an organisation. It can provide valuable insights into customer behaviour, market intelligence and operational performance. Data scientists build intelligent systems to manage, interpret, understand and derive key knowledge from this data. Leveraging the University of Sydney's research strengths, you will explore the latest in data mining, machine learning and data visualisation while developing the skills to communicate data insights to key stakeholders effectively.
This course has no specialisation or major.
For academic requirements check the ‘Admission requirements’ section on this page.
You can apply online via the application portal. When you are ready to apply, select the ‘Apply’ button on this course page. Visit the How to apply page for other important information.
Semester 1 - 31 January of the commencing year
Semester 2 - 15 July of the commencing year
We strongly encourage applicants to apply as early as possible, offers are made on a rolling basis and places are limited. Separate scholarship deadlines apply - check the scholarships website for details.
Semester 1 (February) and Semester 2 (August)
You can apply online via the application portal. When you are ready to apply, select the ‘Apply’ button on this course page. Visit the How to apply page for other important information.
Semester 1 - 15 January of the commencing year
Semester 2 - 25 June of the commencing year
We strongly encourage international applicants to apply as early as possible to allow time for visa and travel arrangements. Separate scholarship deadlines may apply - check the scholarships website for details.
Semester 1 (February) and Semester 2 (August)
To be eligible for admission, an applicant is required to have:
There is no credit transfer possible between the Graduate Certificate in Data Science and the Master of Data Science. Students with a Graduate Certificate in Data Science still need to complete 48 credit points in a subsequent Master of Data Science. They will receive a waiver for COMP5310, and can hence enrol in a third Elective Unit instead.
*A quantitative discipline includes data science, computer science, mathematics, statistics, engineering, physics, economics, finance or other disciplines that are deemed equivalent. As a guideline, the curriculum of a quantitative discipline should include some study of mathematics or statistics at the tertiary level.
Some background in computing is beneficial. For qualifications awarded outside Australia, equivalencies and entry requirements are determined based on the country, institution and qualification. Any grade requirements listed are based on the University of Sydney grading system .
For more information about the University’s credit policy, please see the Credit for Previous Studies page.
The Graduate Certificate in Data Science provides a pathway into the Master of Data Science for students who have a non-quantitative Bachelors degrees. The training it provides also supports several career paths, by: honing data skills at the frontier of machine intelligence research to become a data scientist for hire; upskilling to build intelligent data-driven systems in an existing area of expertise (e.g., mining analyst); improving scientific research through training in data management, analysis, and modelling.
