Graduate Certificate of Data Science

Postgraduate coursework

The Graduate Certificate 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

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

One year part-time

Campuses

Current Deakin Students

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

Course information

The Graduate Certificate of Data Science covers a wide spectrum of subject areas, including modern data science concepts, statistical data analysis, descriptive analytics and machine learning.  You will be equipped with the theory, methodologies, techniques and tools of modern data science and the ability to confidently work with any type of data, to identify trends, make predictions, draw conclusions, drive innovations, make decisions, and to share information that influences people.

This course gives you the essential skills and knowledge for employment across a range of industries and prepares you for further studies in Data Science.

Units in the course may include assessment hurdle requirements.

Read More

Course structure

To complete the Graduate Certificate of Data Science, students must attain 4 credit points. Most units (think of units as ‘subjects’) are equal to 1 credit point. So that means in order to gain 4 credit points, you’ll need to study 4 units (AKA ‘subjects’) over your entire degree. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.

The course comprises a total of 4 credit points, which must include the following:

  • Four (4) core Introductory Data Science Studies units (SIT720, SIT741, SIT742, MIS771) (4 credit points)
  • Completion of STP050 Academic Integrity (0-credit point compulsory unit)

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

Core

Year 1 - Trimester 1

  • Academic Integrity STP050 (0 credit points)
  • Modern Data Science SIT742
  • Descriptive Analytics and Visualisation MIS771
  • Year 1 - Trimester 2

  • Machine Learning SIT720
  • Statistical Data Analysis SIT741
  • Key information

    Award granted
    Graduate Certificate of Data Science
    Year

    2020 course information

    Deakin code
    S577
    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:
      • 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.

    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

    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

    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

    Entry will be based on performance in:

    • Bachelor's degree (AQF7) in a related discipline; OR
    • 2 years relevant work experience; OR
    • Graduate Certificate of Data Analytics (or equivalent); OR
    • Evidence of academic capability judged to be equivalent.

    IELTS / English language requirements

    Please note that English language requirements exist for entry to this course and you will be required to meet the English language level requirement that is applicable in the year of your commencement of studies.

    It is the students’ responsibility to ensure that she/he has the required IELTS score to register with any external accredited courses.  (more details)

    Recognition of prior learning

    Deakin 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 with Deakin, 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 with Deakin.

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

    Recognition of Prior Learning may be granted to applicants based on prior studies and/or equivalent industry experience.

    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 direct to Deakin

    Applications must be made directly to the University through the Course and Scholarship Applicant Portal.  For information on the application process and closing dates, see the Apply web page.  Please note that closing dates may vary for individual courses.

    Apply through Deakin
    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

    PDF Application form - 306 KB


    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

    Further study options:

    Upon completion of the Graduate Certificate of Data Science, you could use the credit points you’ve earned to enter into further study, including:

    S777 Master of Data Science

    Frequently asked questions

    Deakin runs on trimesters, what dates do they each start?

    Find out more about our key dates

    Am I eligible for a scholarship with this course?

    Find our more about scholarships at Deakin

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

    Find out more about RPL

    Where can I study with Deakin?

    Find out more about campus locations

    Why choose Deakin

    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 data analytics solutions based on user requirements by applying coherent knowledge of the analytics discipline using various machine learning and big data analytics tools and techniques.  Apply statistical analysis and visualisation techniques appropriately to interpret analytics outcomes.

    Communication

    Communicate effectively in order to design, evaluate and respond to a range of data analytics problems and utilise a range of verbal, graphical and written forms, customised for diverse audiences.

    Digital literacy

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

    Critical thinking

    Appraise information using logical and analytical thinking to identify user requirements and propose appropriate solutions.

    Problem solving

    Design solutions for automating data analysis processes by applying foundational technical knowledge and tools.

    Self-management

    Work autonomously and responsibly to create solutions to user problems and actively apply fundamental knowledge of data science and methodologies to meet user requirements.

    Teamwork

    Work independently and collaboratively towards achieving the outcomes of a group project.

    Global citizenship

    Engage in professional and ethical behaviour in the collection, processing, and presentation of data.

    Approved by Faculty Board 27 June 2019

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