Master of Data Science

Master of Data Science - eight credit points

To be considered for admission to this degree (with eight credit points of Recognition of Prior Learning applied~), you will need to meet at least one of the following criteria:

• Completion of a graduate certificate or graduate diploma in a related^ discipline
• Completion of a bachelor's honours degree in a related^ discipline
• Completion of a bachelor's degree in a related* discipline and at least two years' of relevant^ work experience (or part-time equivalent).

Master of Data Science - 12 credit points

To be considered for admission to this degree (with four credit points of Recognition of Prior Learning applied~), you will need to meet at least one of the following criteria:

• Completion of a bachelor's degree or higher in a related* discipline
• Completion of a bachelor's degree or higher in any discipline and at least two years' relevant* work experience (or part-time equivalent).

Master of Data Science - 16 credit points

To be considered for admission to this degree, you will need to meet the following criteria:

• Completion of a bachelor's degree or higher in any discipline.

* Related to the broad field of Information Technology.

^ Related to the field of Data Science, which may include artificial intelligence, business analytics, data science and data analytics.

~ Admission credit will be considered case-by-case and may be granted to applicants based on prior studies and/or equivalent industry experience.

To meet the English language proficiency requirements of this course, you will need to demonstrate at least one of the following:

• Bachelor degree from a recognised English-speaking country.
• IELTS overall score of 6.5 (with no band score less than 6.0) or equivalent.
• Other evidence of English language proficiency (learn more about other ways to satisfy the requirements.

• 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 analytics outcomes can be used to improve business, organisations or society.
• Apply advanced knowledge and skills to decompose complex processes (from real-world situations) to develop data analytics solutions for modern organisations across multiple industry sectors.
• Assess the role data analytics plays in the context of modern organisations and society to add value.
• Communicate in professional and other contexts to inform, explain and drive sustainable innovation through data science. Motivate and effect change by drawing upon advances in technology, future trends and industry standards and utilising a range of verbal, graphical and written methods. Recognise the needs of diverse audiences, including specialist and non-specialist clients, industry personnel and other stakeholders.
• 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 the selection of digital tools to influence others.
• 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 data science.
• 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.
• Apply reflective practice and work independently to apply knowledge and skills professionally to complex situations and ongoing learning in data science with adaptability, autonomy, responsibility and personal and professional accountability for actions as a practitioner and a learner.
• Work independently and collaboratively within multidisciplinary environments to achieve team goals, contributing advanced knowledge and skills from data science to advance the team's 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.
• Engage in professional and ethical behaviour in data science, with appreciation for the global context and openly and respectfully collaborate with diverse communities and cultures.

Graduates of this course may find a career as

• Data Analyst
• Data Scientis
• Analytics Programmer
• Analytics Manager
• Analytics Consultant
• Business Analyst
• Management Advisor
• Management Analyst
• Business Advisors
• Strategist
• Marketing Manager
• Market Research Analyst
• Marketing Specialist