Master of Data Science

2025 Deakin University Handbook

Year

2025 course information

Award granted Master of Data Science
Deakin course codeS777
Faculty

Faculty of Science, Engineering and Built Environment

Campus

For students who commenced prior to 2022

Duration

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

  • 1 year full-time (2 years part-time) - 8 credit points
  • 1.5 years full-time (3 years part-time) - 12 credit points
  • 2 years full-time (4 years part-time) - 16 credit points
CRICOS course code099225J Burwood (Melbourne)
Australian Qualifications Framework (AQF) recognition

The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9

The final intake to this course version was in 2021.

Students should contact a Student Adviser in Student Central for course and enrolment information.

Further course structure information can be found in the handbook archive.

Course sub-headings

Course overview

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. With modern organisations placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions, Deakin’s Master of Data Science equips you for a career in this fast-growing sector.

Throughout your studies you will gain the technical skills to harness the power of data through artificial intelligence and machine learning. Use your insights to develop innovative solutions to the important challenges being faced by industry and governments. With a growing demand for data specialists in every sector, you will be able to help organisations manage risk, optimise performance and add a competitive advantage through the increasing volumes of data collection.

Want to become a data science specialist capable of using data to learn insights and support decision making?

The Master of Data Science prepares you 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 will 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.

Through the Master of Data Science you can choose to undertake an industry placement or internship as part of your degree. Industry placements provide you with an opportunity to develop the practical and job-ready skills employers are looking for and enable you to build professional networks before graduating.

Indicative student 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.

Professional recognition

The Master of Data Science is professionally accredited with the Australian Computer Society (ACS).

Career opportunities

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.

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. More information available at Disability support services.

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

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.

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

Graduate Diploma of Data Science (S677)

Course Learning Outcomes

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

Course rules

To complete the Master of Data Science) students must pass 8, 12 or 16 credit points and meet the following course rules to be eligible to graduate. 

  • DAI001 Academic Integrity and Respect at Deakin (0-credit-point compulsory unit) in their first study period

The course is structured in 3 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. See the enrolment codes and terminology to help make sense of the University’s vocabulary. 

Course structure

Core

Mandatory unit for all entry levels *

STP050 Academic Integrity (0 credit points) [No longer available for enrolment from 2024, alternate unit DAI001]

Part A: Fundamental Data Analytics Studies

MIS770Foundation Skills in Data Analysis

SIT718Real World Analytics

SIT719Analytics for Security and Privacy

SIT740Research and Development in Information Technology

Part B: Introductory Data Science Studies

SIT720Machine Learning

SIT741Statistical Data Analysis

SIT742Modern Data Science

MIS771Descriptive Analytics and Visualisation

Part C: Mastery Data Science Studies

SIT743Bayesian Learning and Graphical Models

SIT744Deep Learning

SIT764Team Project (A) - Project Management and Practices ~

SIT782Team Project (B) - Execution and Delivery ~

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

SIT791Professional Practice (4cp)
and

STP710Career Tools for Employability (0cp)

OR

SIT723Research Techniques and Applications (2cp)
AND

2 additional credit points of level 7 SIT units#

OR

SIT723Research Techniques and Applications (2cp)
AND

SIT724Research Project (2cp)

OR

4 credit points of level 7 SIT/MIS coded units#

~ 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

* DAI001 Academic Integrity and Respect at Deakin replaces STP050 Academic Integrity, from 2024.

Work experience

You may have an opportunity to undertake a placement as part of your course. For more information, please visit deakin.edu.au/sebe/wil.


Course duration

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

Further information

Student Central can help you with course planning, choosing the right units and explaining course rules and requirements.

Other learning experiences

You may choose to use one of your elective units to undertake an internship or participate in an overseas study tour to enhance your global awareness and experience.

Fees and charges

Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study, and their study discipline or your study load.

Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.

Estimate your fees

For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current students website.