2 years part-time (includes the 11 month or 12 month pathway program via Great Learning and 1 year part-time Deakin content)
This course is delivered by Great Learning wholly Online.
Current Deakin Students
To access your official course details for the year you started your degree, please visit the handbook
Indian nationals residing in India are encouraged to consider the Master of Data Science (Global). Designed for IT professionals interested in gaining a master level qualification in emerging technology areas including machine learning, data science and AI, this course is offered in partnership with Great Learning and builds upon relevant postgraduate programs to provide graduates with an opportunity to extend their learning in data science through completion of in-depth studies at Deakin.
Students who have successfully completed Great Learning’s Postgraduate Program in Artificial Intelligence and Machine Learning or Postgraduate Program in Data Science and Business Analytics and satisfy the entry requirements for this course, will undertake further studies at Deakin. This provides students with the opportunity to extend their understanding of data science through focused studies in machine learning, data wrangling, AI and applied analytics. Students will learn how to analyse and prepare data to create meaningful insights through real-world projects.Read More
- Award granted
- Master of Data Science (Global)
2023 course information
- Deakin code
- 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.
To qualify for the award of Master of Data Science (Global) students must complete 12 credit points comprising 8 credit points of core units and 4 credit points of elective units.
Graduates of the Post Graduate Program in Artificial Intelligence and Machine Learning (PGPAIML) or Post Graduate Program in Data Science and Business Analytics (PGPDSBA) who have successfully completed Great Learning Units equivalent to 6 credit points as recognised by Deakin; and will have met the minimum requirements for admission to Deakin will be eligible for enrolment into the Deakin Course with 6 credit points of recognition of prior learning and will be required to successfully complete 6 Deakin Units with Deakin University in online mode in order to qualify for the Deakin Master of Data Science (Global) Award. i.e.
- 6 x Deakin units
- 6 x RPL
The Deakin component of the structure consists of all existing units which will be delivered online over a period of a year (3 x Trimesters). Students will enrol part-time, undertaking 2 units (2 credit points) each Trimester. Outlined below are the units.
Recognition For Prior Learning (based on Great Learning programs)
4 x Level 7 course-grouped units
^ Recognition for Prior Learning (RPL) granted upon entry into the course
Intakes by location
The availability of a course varies across locations and intakes. This means that a course offered in Trimester 1 may not be offered in the same location for Trimester 2 or 3. Check each intake for up-to-date information on when and where you can commence your studies.
This course is only available to students via the Great Learning pathway. This course is not available to international students studying onshore in Australia. This course is offered part-time only.
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.
- Successful completion of one of the postgraduate international articulation programs: Great Learning Postgraduate Program in Data Science and Business Analytics (11 month version) OR Postgraduate Program in Machine Learning and Artificial Intelligence (12 month version) AND
- Bachelor degree in a related discipline (AQF level 7 equivalent) OR Bachelor degree in any discipline (AQF level 7 equivalent) and two years relevant work experience.
All applicants must meet the minimum English language requirements.
Please note that meeting the minimum admission requirements does not guarantee selection, which is based on merit, likelihood of success and availability of places in the course.
For more information on the Admission Criteria and Selection (Higher Education Courses) Policy visit the Deakin Policy Library
Applications can be made directly to Great Learning. (Note this link is for Great Learning applicants based in India. It is unavailable when accessing from Australia). For more information on the application process and closing dates, please email Great Learning or call +91 804 718 7565.
The Master of Data Science (Global) builds upon the Postgraduate Programs from Great Learning with units that extend students into the data science area. Units within the Deakin delivered content are independent of each other and provide coverage of the mathematical foundations that underpin data science, machine learning, engineering IT solutions that incorporate Artificial Intelligence, preparation of data, and analytics for real world projects.
Interested in advancing in your current employment or expanding your career opportunities? The Master of Data Science (Global) provides a master level qualification in emerging technology areas including machine learning, data science and AI to equip you with the specialist skills required in modern workplace settings. 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.
Course learning outcomes
Deakin's graduate learning outcomes describe the knowledge and capabilities graduates can demonstrate at the completion of their course. These outcomes mean that regardless of the Deakin course you undertake, you can rest assured your degree will teach you the skills and professional attributes that employers value. They'll set you up to learn and work effectively in the future.
Deakin Graduate Learning Outcomes
Course Learning Outcomes
Discipline-specific knowledge and capabilities
|Develop a broad, coherent knowledge of the analytics discipline, including: the origin and characteristics of data; the methods and approaches to dealing with data appropriately and securely; and how the use of analytics outcomes can be used to improve business, organisations or society.|
Utilise a range of verbal, graphical and written forms, customised to communicate for diverse audiences including specialist and non- specialist clients, colleagues and industry personnel.
Utilise a range of digital technologies and information sources to discover, select, analyse, synthesise, evaluate, critique, and disseminate both technical and professional information.
|Appraise complex information using critical and analytical thinking and judgement to identify problems, analyse user requirements and propose appropriate and innovative solutions.|
Generate data solutions through the application of specialised theoretical constructs, expert skills and critical analysis to real-world, ill-defined problems to develop appropriate and innovative IT solutions.
|Take personal, professional and social responsibility within changing national and international professional IT contexts to develop autonomy as data scientists and evaluate own performance for continuing professional development.|
Work independently and collaboratively towards achieving the outcomes of a group project, and function effectively in diverse professional, social and cultural contexts.
|Engage in professional and ethical behaviour in the design, development and management of data analysis solutions, in the global context, in collaboration with diverse communities and cultures.|
Approved by Faculty Board 31 March 2022