Master of Data Science (Global)

2025 Deakin University Handbook

Year	2025 course information
Award granted	Master of Data Science (Global)
Deakin course code	S773
Faculty	Faculty of Science, Engineering and Built Environment
Campus	This course is delivered by Great Learning online.
Duration	2 years part-time (includes the 11 month or 12 month pathway program via Great Learning and 1 year part-time Deakin content)
Australian Qualifications Framework (AQF) recognition	The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9
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.

Course sub-headings

Course overview

Indian nationals residing in India are encouraged to consider the Master of Data Science (Global). This program is designed for IT professionals seeking a masters qualification in emerging technology fields such as machine learning, data science, and AI. Offered in partnership with Great Learning, the degree builds upon relevant postgraduate programs. This provides graduates the opportunity to extend their learning in data science through advanced 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 enables students 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.

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

Interested in advancing in your current employment or expanding your career opportunities? The Master of Data Science (Global) provides a masters level qualification in emerging technology areas like machine learning, data science and AI. This program equips you with the specialist skills required in modern workplaces. Graduates of this course may find careers as a data analyst, data scientist, analytics programmer, analytics manager, analytics consultant, business analyst, management advisor, management analyst, business advisor and strategists, marketing manager, market research analyst or marketing specialists.

Pathways

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, and IT solutions that incorporate artificial intelligence, preparation of data, and analytics for real world projects.

Course Learning Outcomes

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.
Communication	Communicate in professional and other context to inform, explain and drive sustainable innovation through data science and to motivate and effect change by drawing upon advances in technology, future trends and industry standards, and by utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences including specialist and non-specialist clients, industry personnel and other stakeholders.
Digital literacy	Identify, evaluate, select and use digital technologies, platforms, frameworks, and tools from the field of data science to generate, manage, process and share digital resources and justify digital tools selection to influence others.
Critical thinking	Questions assumptions and seeks to uncover inconsistencies and ambiguities in information and judgements, critically evaluates their sources and rationales, to inform and justify decision making in the field of data science.
Problem solving	Apply expert, specialised cognitive, technical, and creative skills from data science to understand requirements and design, implement, operate, and evaluate solutions to complex real-world and ill-defined computing problems.
Self-management	Apply reflective practice and work independently to apply knowledge and skills in a professional manner to complex situations and ongoing learning in the field of data science with adaptability, autonomy, responsibility, and personal and professional accountability for actions as a practitioner and a learner.
Teamwork	Work independently and collaboratively within multidisciplinary environments to achieve team goals, contributing advanced knowledge and skills from data science to advance the teams objectives, employing effective teamwork practices and principles to cultivate creative thinking, interpersonal adeptness, leadership skills, and handle challenging discussions, while excelling in diverse professional, social, and cultural scenarios.
Global citizenship	Engage in professional and ethical behaviour in the field of data science, with appreciation for the global context, and openly and respectfully collaborate with diverse communities and cultures.

Course rules

To complete the Master of Data Science (Global) students must pass 12 credit points and meet the following course rules to be eligible to graduate:

DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in their first study period
8 credit points of core units
4 credit points of elective units

Students are required to meet the University's academic progress and conduct requirements. See the enrolment codes and terminology to help make sense of the University’s vocabulary.

Graduates of the Postgraduate 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 (RPL) and will be required to successfully complete 6 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.