Graduate Diploma of Data Science

Postgraduate coursework

The Graduate Diploma of Data Science covers a wide spectrum of subject areas, including modern data science concepts, statistical data analysis, descriptive analytics and machine learning.

Key facts

Duration

2 years part-time

Current Deakin Students

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

Course information

The Graduate Diploma of Data Science covers modern data science concepts, statistical data analysis, descriptive analytics and machine learning to equip you with the theory, methodologies, techniques and tools of modern data science. Through this course, you will develop the ability to confidently work with any type of data, to identify trends, make predictions, draw conclusions, drive innovations, make decisions and share information that influences people.

The sheer volume and complexity of data already available to businesses gives rise to challenges that must be solved by tomorrow’s graduates. Employers are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions. This course gives you essential skills in data analytics, enabling you to discover insights and support decision-making across a range of industries.

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Course structure

To complete the Graduate Diploma of Data Analytics, students must attain 8 credit points.

The course is structured in two parts:

  • Part A: Fundamental Data Analytics Studies (4 credit points),
  • Part B. Core Data Science Studies (4 credit points), plus
  • Completion of STP050 Academic Integrity (0-credit point compulsory unit)

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

Core

Mandatory unit for all entry levels

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

  • Real World Analytics
  • Data Wrangling
  • Mathematics for Artificial Intelligence
  • Plus one level 7 SIT or MIS coded unit#

    Part B: Core Data Science Studies

  • Machine Learning
  • Statistical Data Analysis
  • Modern Data Science
  • Plus one level 7 SIT or MIS coded unit#

    # Excluding SIT771, SIT772, SIT773, SIT774

    Key information

    Award granted
    Graduate Diploma of Data Science
    Year

    2022 course information

    Deakin code
    S677
    Level
    Postgraduate (Graduate Certificate and Graduate Diploma)
    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 8.

    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

    INTERNATIONAL STUDENTS – Please note that due to Australian Government regulations, student visas to enter Australia cannot be issued to students who enrol in Deakin’s 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.

    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.

    Fees and scholarships

    Fee information

    Estimated tuition fee - full-fee paying place
    The estimated fee for this course is not currently available, please contact Student Central for further information.
    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.

    Contact information

    Faculty of Science, Engineering and Built Environment
    School of Information Technology
    Tel 03 9244 6699
    sebe@deakin.edu.au

    www.deakin.edu.au/information-technology

    Careers

    Career outcomes

    Graduates of this course are prepared for professional employment across all sectors as data science specialists. Professionals with a solid knowledge in data science and strong skills for analysing and interpreting data in today's data-rich economy are in high demand and 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 specialised knowledge of data analytics concepts and technologies to solutions based on specifications and user requirements. 

    Communication

    Communicate data analytical solutions as appropriate to the context to inform, motivate and effect change utilising a range of verbal, graphical and written methods, recognising the needs of diverse audiences. 

    Digital literacy

    Use digital media to locate, collect and evaluate information from technical channels and apply information to design approaches and solutions that meet user requirements.

    Critical thinking

    Use the frameworks of logical and analytical thinking to evaluate data analytics information, technical problems and user requirements, and develop approaches to identify solutions. 

    Problem solving

    Design solutions for automating data analysis processes by investigating technical and business problems; design and propose alternative solutions that improve services and user experiences.

    Self-management

    Demonstrate the ability to work in a professional manner, learn autonomously and responsibly in order to identify and meet development needs.

    Global citizenship

    Engage in professional and ethical behaviour in the design of data analytics systems, in a global context, in collaboration with diverse communities and cultures.

    Approved by Faculty Board 27 June 2019