Master of Data Science (Professional)

Postgraduate coursework

Become a data specialist capable of using data to form insights, support decision making and create a competitive advantage in today's business world.

Key facts

Duration

2 years full-time or part-time equivalent

Key dates

Direct applications to Deakin for Trimester 1 2022 close 20 February 2022

Current Deakin Students

To access your official course details for the year you started your degree, please visit the handbook

Course information

The sheer volume and complexity of data at the fingertips of business gives rise to challenges that must be solved by tomorrow's graduates. Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions. You will explore the various origins of data and the methods to manage, organise and manipulate data within regulatory, ethical and security constraints.

You will develop specialised skills in categorising and transferring raw data into meaningful information for the benefit of prediction and robust decision-making. You will gain the technical skills to harness the power of data through artificial intelligence and machine learning to develop innovative solutions to the challenges being faced by industry and governments. With a growing demand for data specialists in every sector, you will be equipped with the skills to optimise performance and add a competitive advantage.

Want to take your career to the next level with specialised study?

The Master of Data Science (Professional) is designed to extend the specialised skills obtained in the Master of Data Science by providing you with the opportunity to undertake a period of industry-based learning or a research project under the supervision of our internationally-recognised staff.

You will also have the opportunity to hone your skills is a specialisation of your choosing, with options ranging from cyber security to blockchain and software development, networking and cloud technologies to AI and more.

You will develop expert knowledge of the technical aspects of data science as well as in-depth skills in your chosen area of specialisation.

Read More

Course structure

To complete the Master of Data Science (Professional), students must attain 16 credit points. Full time students’ study 4 credit points per trimester, and usually undertake two trimesters each year.

The course is structured in three parts:

  • Part A. Core Data Science Studies (8 credit points),
  • Part B. Specialisation (4 credit points), and
  • Part C. Professional Studies (4 credit points)

The three parts comprise the following:

  • eight (8) credit points of core units,
  • four (4)-credit point specialisation, or four (4) credit points of course grouped electives (level 7 SIT or MIS coded units)
  • four (4) credit points of professional units
  • completion of STP050 Academic Integrity (0-credit-point compulsory unit)

Core

Mandatory unit for all entry levels

  • Academic Integrity (0 credit points)
  • Part A: Core Data Science Studies

  • Real World Analytics
  • Data Wrangling
  • Mathematics for Artificial Intelligence
  • Machine Learning
  • Statistical Data Analysis
  • Modern Data Science
  • Bayesian Learning and Graphical Models
  • Deep Learning
  • Part B: Specialisation or course grouped electives

    Four (4) core units from a chosen specialisation (four credit points),

    OR

    Four (4) course grouped electives (level 7 SIT or MIS coded units)#

    Part C: Professional Studies

  • Team Project (A) - Project Management and Practices ~
  • Team Project (B) - Execution and Delivery ~
  • and two credit points of level 7 SIT electives#
    OR

  • Professional Practice (4 credit points)
  • Career Tools for Employability (0 credit points)
  • OR

  • Research Project A (2 credit points)
  • and two credit points of level 7 SIT electives#
    OR

  • Research Project A (2 credit points)
  • Research Project B (Thesis) (2 credit points)
  • ~ Note: Students are recommended to undertake SIT764 and SIT782 in consecutive trimesters. Students should seek advice from the unit chair if they are unable to complete SIT764 and SIT782 consecutively.
    # Excluding SIT771, SIT772, SIT773, SIT774

    Specialisations

    Refer to the details of each specialisation for availability.

    Key information

    Award granted
    Master of Data Science (Professional)
    Year

    2022 course information

    Deakin code
    S770
    CRICOS code?
    107030E Burwood (Melbourne)
    Level
    Higher Degree Coursework (Masters and Doctorates)
    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.

    Campuses by intake

    Campus availability varies per trimester. This means that a course offered in Trimester 1 may not be offered in the same location for Trimester 2 or 3. Read more to learn where this course will be offered throughout the year.

    Trimester 1 - March

    • Start date: March
    • Available at:
      • Burwood (Melbourne)
      • Cloud Campus

    Trimester 2 - July

    • Start date: July
    • Available at:
      • Burwood (Melbourne)
      • Cloud Campus

    Additional course information

    Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.

    Mandatory student checks

    Any unit which contains work integrated learning, a community placement or interaction with the community may require a police check, Working with Children Check or other check.

    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.

    Participation requirements

    Elective units may be selected that include compulsory placements, work-based training, community-based learning or collaborative research training arrangements.

    Reasonable adjustments to participation and other course requirements will be made for students with a disability. Click here for more information.

    Work experience

    You will have an opportunity to undertake a placement as part of your course.

    Entry requirements

    Entry information

    • Bachelor’s degree in a related discipline, OR
    • Bachelor’s degree in any discipline and two years relevant work experience OR
    • Graduate Certificate of Information Technology OR
    • Evidence of academic capability judged to be equivalent.

    Deakin University offers admission to postgraduate courses through a number of Admission categories.

    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

    Recognition of prior learning

    The University aims to provide students with as much credit as possible for approved prior study or informal learning which exceeds the normal entrance requirements for the course and is within the constraints of the course regulations. Students are required to complete a minimum of one-third of the course at Deakin University, or four credit points, whichever is the greater. In the case of certificates, including graduate certificates, a minimum of two credit points within the course must be completed at Deakin.

    You can also refer to the Recognition of Prior Learning System which outlines the credit that may be granted towards a Deakin University degree and how to apply for credit.

    Recognition of Prior Learning may be granted for relevant postgraduate studies, in accordance with standard University procedures.

    Fees and scholarships

    Fee information

    Estimated tuition fee - full-fee paying place
    $28,600 for 1 yr full-time - Full-fee paying place
    Learn more about fees and your options for paying.

    Fees and charges vary depending on your course, your fee category and the year you started. To find out about the fees and charges that apply to you, visit www.deakin.edu.au/fees.

    Scholarship options

    A Deakin scholarship might change your life. If you've got something special to offer Deakin – or you just need the financial help to get you here – we may have a scholarship opportunity for you.

    Search or browse through our scholarships

    Postgraduate bursary

    If you’re a Deakin alumnus commencing a postgraduate award course, you may be eligible to receive a 10% reduction per unit on your enrolment fees.

    Learn more about the 10% Deakin alumni discount

    Apply now

    How to apply

    Apply direct to Deakin

    Applications can be made directly to the University through the Deakin Application Portal. For information on the application process and closing dates, see the how to apply web page. Please note that closing dates may vary for individual courses.

    Need more information on how to apply?

    For more information on the application process and closing dates, see the How to apply webpage. If you're still having problems, please contact us for assistance.

    Entry pathways

    Pathways for students to enter the Master of Data Science (Professional) are as follows:

    • Graduate Certificate of Information Technology (followed by a 16 credit point Master of Data Science (Professional))
    • Graduate Certificate of Data Analytics (followed by a 12 credit point Master of Data Science (Professional))
    • Graduate Diploma of Data Science (followed by a 8 credit point Master of Data Science (Professional))
    • Master of Data Science (followed by a 4 credit point Master of Data Science (Professional))

    Pathway options will depend on your professional experience and previous qualifications.

    Contact information

    Faculty of Science, Engineering and Built Environment
    School of Information Technology
    deakin.edu.au/information-technology

    Prospective student enquiries
    Are you looking to apply for this course or would like further information?
    Call 1800 693 888 or email us at myfuture@deakin.edu.au
    Enquire online

    Current student course and enrolment enquiries
    Call 03 9244 6699 or email us at sebe-enquire@deakin.edu.au
    Submit an online enquiry

    Careers

    Career outcomes

    In fiercely competitive markets where businesses are constantly striving to increase profit, reduce costs and provide exceptional customer value, the requirement for skilled data professionals is growing at a rapid pace. 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.  Apply advanced knowledge and skills to decompose complex processes (from real world situations) to develop data analytics solutions for use in modern organisations across multiple industry sectors.  Assess the role data analytics plays in the context of modern organisations and society in order to add value. Have a broad appreciation of advanced topics within the IT domain through engagement with research or specialist studies.

    Communication

    Communicate effectively in order to design, evaluate and respond to advances in data analytics approaches, technology, future trends and industry standards and utilise a range of verbal, graphical and written forms, customised for diverse audiences including specialist and non- specialist clients, colleagues and industry personnel.

    Digital literacy

    Utilise a range of digital technologies and information sources to discover, select, analyse, synthesise, evaluate, critique and disseminate both technical and professional information.

    Critical thinking

    Appraise complex information using critical and analytical thinking and judgement to identify problems, analyse user requirements and propose appropriate and innovative solutions.

    Problem solving

    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.

    Self-management

    Take personal, professional and social responsibility within changing national and international professional IT contexts to develop autonomy as researchers and evaluate own performance for continuing professional development.  Work autonomously and responsibly to create solutions to new situations and actively apply knowledge of theoretical constructs and methodologies to make informed decisions.

    Teamwork

    Work independently and collaboratively towards achieving the outcomes of a group project, thereby demonstrating interpersonal skills including the ability to brainstorm, negotiate, resolve conflicts, manage difficult and awkward conversations, provide constructive feedback, and demonstrate the ability to function effectively in diverse professional, social and cultural contexts.

    Global citizenship

    Engage in professional and ethical behaviour in the design, development and management of IT systems, in the global context, in collaboration with diverse communities and cultures.

    Approved by Faculty Board 27 June 2019

    * 2019 Student Experience Survey, based on undergraduate students
    # ARWU Rankings 2019
    ~ According to the Voice Project IT Service Quality Support Benchmark Survey
    ^ Australian Graduate Recruitment Industry Awards, 2017, 2018, 2019 winner
    ^^ Australian Graduate Survey 2010–2015, Graduate Outcomes Survey 2016–2019 (GOS), Quality Indicators for Learning and Teaching (QILT)