Master of Data Science

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

English language requirements

Overall IELTS score of 6.5 with no band less than 6 (or equivalent). More information is available at www.ielts.org

Duration

Depending on your professional experience and previous qualifications, your course will be:

  • 1 year full time (2 years part time) – 8 credits
  • 1.5  years full time (3 years part time) – 12 credits
  • 2 years full time (4 years part time) – 16 credits

Current Deakin Students

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

Course information

Deakin’s Master of Data Science prepares students for professional employment across all sectors.  The sheer volume and complexity of data already at the fingertips of businesses and research organisations gives rise to challenges that must be solved by tomorrow’s graduates.  Become a data analytics specialist capable of using data to learn insights and support decision making.

Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions.

Throughout your studies you’ll learn to understand the various origins of data to be used for analysis, combined with methods to manage, organise and manipulate data within regulatory, ethical and security constraints. You’ll develop specialised skills in categorising and transferring raw data into meaningful information for the benefit of prediction and robust decision-making.

As a graduate, your knowledge, skills and competencies in modern data science and statistical analysis will be highly valued by employers seeking greater efficiencies and competitive advantage through data insights.

Units in the course may include assessment hurdle requirements.

Read More

Course structure

To complete the Master of Data Science, you will complete 8, 12 or 16 credit points, depending on your prior experience.

The course is structured in three parts:

  • Part A. Fundamental Data Analytics Studies (4 credit points),
  • Part B. Introductory Data Science Studies (4 credit points), and
  • Part C. Mastery Data Science Studies (8 credit points).

Depending upon prior qualifications and/or experience, you may receive credit for Parts A and B.

Note that if you are eligible for credit for prior studies you may elect not to receive the credit.

Students are required to meet the University's academic progress and conduct requirements. Click here for more information.

Core

Mandatory unit for all entry levels

  • Academic Integrity STP050 (0 credit points)
  • Part A: Fundamental Data Analytics Studies

  • Foundation Skills in Data Analysis MIS770
  • Real World Analytics SIT718
  • Security and Privacy Issues in Analytics SIT719
  • Research and Development in Information Technology SIT740

  • Part B: Introductory Data Science Studies

  • Machine Learning SIT720
  • Statistical Data Analysis SIT741
  • Modern Data Science SIT742
  • Descriptive Analytics and Visualisation MIS771
  • Part C: Mastery Data Science Studies

  • Bayesian Learning and Graphical Models SIT743
  • Deep Learning SIT744
  • Team Project (A) - Project Management and Practices SIT764 ~
  • Team Project (B) - Execution and Delivery SIT782 ~
  • ~ Note: Students are expected 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.

    Electives should be selected from one of the following combinations:

    Four (4) credit points of project/thesis/placement units from the list below:

  • Major Thesis SIT790 (4 cp), or
  • Professional Practice SIT791 (4 cp)*, or
  • Minor Thesis SIT792 (2 cp), and
  • 2 additional level 7 SIT/MIS-coded elective units or

  • Internship - Information Technology SIT709 (1 cp)*, and 3 additional level 7 SIT/MIS-coded elective units
  • *Students undertaking this unit must have successfully completed STP710 Career Tools for Employability (0-credit point unit)

    Key information

    Award granted
    Master of Data Science
    Year

    2020 course information

    Deakin code
    S777
    CRICOS code?
    099225J
    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

    Trimester 3 - November

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

    Additional course information

    Course duration - additional 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.

    Students commencing in Trimester 3 will be required to complete units in Trimester 3.

    Work experience

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

    Entry requirements

    Entry information

    Deakin University offers admission to postgraduate courses through a number of Admission categories. To be eligible for admission to this program, applicants must meet the course requirements.

    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

    Admission to study postgraduate coursework at Deakin is based on your professional experience and previous academic qualifications. Depending on your prior experience, your course will be:

    1 year full time (2 years part time) – 8 credit points

    Admission is based on:

    • Bachelor’s Honours (AQF8) degree in a related discipline
    • Bachelor’s degree in a related discipline, plus two years relevant work experience
    • Graduate Certificate of Data Science (or equivalent)
    • Evidence of academic capability judged to be equivalent

    1.5 years full time (3 years part time) – 12 credit points

    Admissions is based on:

    • Bachelor’s degree in a related discipline
    • Bachelor’s degree in any discipline, plus two years relevant work experience
    • Graduate Certificate of Data Analytics or equivalent
    • Evidence of academic capability judged to be equivalent

    2 years full time (4 years part time) – 16 credit points

    Admissions is based on:

    • Bachelor’s degree or other qualification at a higher AQF level in any discipline
    • Evidence of academic capability judged to be equivalent

    Recognition of prior learning

    If you have completed previous studies which you believe may reduce the number of units you have to complete at Deakin, indicate in the appropriate section on your application that you wish to be considered for Recognition of Prior Learning. You will need to provide a certified copy of your previous course details so your credit can be determined. If you are eligible, your offer letter will then contain information about your Recognition of Prior Learning.
    Your Recognition of Prior Learning is formally approved prior to your enrolment at Deakin during the Enrolment and Orientation Program. You must bring original documents relating to your previous study so that this approval can occur.

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

    Fees and scholarships

    Fee information

    Estimated tuition fee - full-fee paying place

    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 15% reduction per unit on your enrolment fees. Your Immediate Family Members may also be eligible to apply for this bursary.

    Learn more about Deakin’s 15% postgraduate bursary

    Apply now

    How to apply

    Apply through Deakin

    Applications can be made directly to the University through StudyLink Connect - Deakin University's International Student Application Service. 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.


    Deakin International office or Deakin representative

    Fill out the application form and submit to a Deakin International office or take your application form to a Deakin representative for assistance


    Need more information on how to apply?

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


    Entry pathways

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

    • Option 1: Graduate Certificate of Data Analytics (S576) (followed by a 12 credit point Master of Data Science)
    • Option 2: Graduate Certificate of Data Science (S577) (followed by an 8 credit point Master of Data Science)

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

    Alternative exits

    Frequently asked questions

    What are the key study start dates?

    Browse all start and finish dates for Deakin’s main study periods. You’ll also find dates relating to applications and prospective student events, plus a list of all public holidays and study breaks.

    How much does it cost to study at Deakin?

    Your tuition fees will depend on the type of student you are, the course you study and the year you start. Fees are based on an annual amount; they don't cover the entire duration of the course.

    Use our fee estimator to gauge what your fees could be per year.

    Can I speak to someone in person about my study options?

    Yes! We regularly host a range of events including 1:1 consultations and information sessions, to assist you with your study options and career planning. Check out our upcoming events or contact our Prospective Student Enquiry Centre on 1800 693 888 for more information.

    Am I eligible for a scholarship with this course?

    Scholarships are available for domestic and international students at all study levels. Find a scholarship that works for you.

    Can I claim recognition of prior learning (RPL) for this course?

    In some courses, you can reduce your overall study time and tuition cost by getting your work and previous study experience recognised as recognition of prior learning (RPL).

    Why choose Deakin

    Career outcomes

    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.

    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

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