Master of Data Science
Course summary for local students
| Year | 2024 course information |
|---|---|
| Award granted | Master of Data Science |
| Campus | Offered at Burwood (Melbourne) |
| Online | Yes |
| Length | The time and cost could be reduced based on your previous qualifications and professional experience. This means you can fast track the masters degree from 2 years down to 1.5 years, or even 1 year duration. See entry requirements below for more information. |
| CSP annual fee (indicative) - commencing 2024 | $8,385 for 1 yr full-time - Commonwealth Supported Place (HECS) |
| Full fee paying annual fee - commencing 2024 | $31,000 for 1 yr full-time - Full-fee paying place |
| Level | Higher Degree Coursework (Masters and Doctorates) |
| Faculty contacts | Our friendly advisers are available to speak to you one-on-one about your study options, support services and how we can help you further your career.
|
| CRICOS course code | 099225J Burwood (Melbourne) |
| Deakin course code | S777 |
| Australian Qualifications Framework (AQF) recognition | The award conferred upon completion is recognised in the Australian Qualifications Framework at Level 9 |
Course sub-headings
- Course overview
- Professional recognition
- Fees and charges
- Career opportunities
- Course Learning Outcomes
- Course rules
- Course structure
- Admission criteria
- Academic requirements
- English language proficiency requirements
- Admissions information
- Pathways
- Credit for prior learning - general
- Credit for prior learning - specific
- Alternate exits
- Workload
- Work experience
- Participation requirements
- Mandatory student checks
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.
Professional recognition
The Master of Data Science is professionally accredited with the Australian Computer Society (ACS).
Fees and charges
The available fee places for this course are detailed above. Not all courses at Deakin have Commonwealth supported places available. The 'Estimated tuition fee' is provided as a guide only based on a typical enrolment of students completing the first year of this course. The cost will vary depending on the units you choose, your study load, the length of your course and any approved Recognition of prior learning.
One year full-time study load is typically represented by eight credit points of study. Each unit you enrol in has a credit point value. The 'Estimated tuition fee' is calculated by adding together eight credit points of a typical combination of units for your course.
You can find the credit point value of each unit under the Unit Description by searching for the unit in the Handbook.
Learn more about fees and available payment options.
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.
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 attain 8, 12 or 16 credit points, depending on your prior experience. Most students choose to study 4 units per trimester, and usually undertake two trimesters each year.
The course is structured in four parts:
- Part A: Foundation Information Technology Studies (4 credit points)
- Part B: Fundamental Data Analytics Studies (4 credit points),
- Part C: Core Data Science Studies (4 credit points), and
- Part D: Mastery Data Science Studies (4 credit points), plus
- Completion of DAI001* Academic Integrity Module (0-credit point compulsory unit)
Depending upon prior qualifications and/or experience, you may receive credit for Foundation Information Technology and/or Fundamental Data Analytics Studies.
Note that if you are eligible for credit for prior studies you may elect not to receive the credit.
Course structure
Core
Mandatory unit for all entry levels
| DAI001 | Academic Integrity Module (0 credit points) |
Part A: Foundation Information Technology Studies
| SIT771 | Object-Oriented Development |
| SIT772 | Database Fundamentals |
| SIT773 | Software Requirements Analysis and Modelling |
| SIT774 | Web Technologies and Development |
| SIT718 | Real World Analytics |
| SIT731 | Data Wrangling |
| SIT787 | Mathematics for Artificial Intelligence |
Plus one level 7 SIT or MIS elective
Part C: Core Data Science Studies
| SIT720 | Machine Learning |
| SIT741 | Statistical Data Analysis |
| SIT742 | Modern Data Science |
| SIT753 | Professional Practice in Information Technology |
Part D: Mastery Data Science Studies
| SIT743 | Bayesian Learning and Graphical Models |
| SIT744 | Deep Learning |
| SIT764 | Team Project (A) - Project Management and Practices |
| SIT782 | Team Project (B) - Execution and Delivery ~ |
~ 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.
Admission criteria
Selection is based on a holistic consideration of your academic merit, work experience, likelihood of success, availability of places, participation requirements, regulatory requirements, and individual circumstances. You will need to meet the minimum academic and English language proficiency requirements to be considered for selection, but this does not guarantee admission.
Depending on your professional experience and previous qualifications, you may commence this course with admission credit and complete your course in 1 year full-time (or part-time equivalent).
The time and cost of your course could be reduced based on your previous qualifications and professional experience. This means the duration of the masters degree could be reduced from 2 years down to 1.5 years, or even 1 year duration. See academic requirements below for more information.
Academic requirements
1 year full-time (or part-time equivalent) - 8 credit points
To be considered for admission to this degree (with 8 credit points of admission credit applied) you will need to meet at least one of the following criteria:
- completion of a bachelor degree (honours) (AQF 8) or higher in a related discipline
- completion of a bachelor degree in a related discipline, and at least two years' of relevant work experience (or part-time equivalent)
- completion of a bachelor degree in a related discipline and Graduate Certificate of Data Analytics or equivalent
- Graduate Certificate of Information Technology and Graduate Certificate of Data Analytics
1.5 years full-time (or part-time equivalent) - 12 credit points
To be considered for admission to this degree (with 4 credit points of admission credit applied) you will need to meet at least one of the following criteria:
- completion of a bachelor degree or higher in a related discipline
- completion of a bachelor degree or higher in any discipline, and at least two years' of relevant work experience (or part-time equivalent)
- Graduate Certificate of Information Technology (or equivalent)
2 years full-time (or part-time equivalent) - 16 credit points
To be considered for admission to this degree (without admission credit applied*) you will need to meet the following criteria:
- completion of a bachelor degree or higher in any discipline
^Recognition of Prior Learning into the Master of Data Science may be granted to students who have successfully completed appropriate postgraduate level studies.
Related disciplines which may be considered include: information technology, computing, computer science, software engineering.
*Credit for recognition of prior learning will still be considered on a case-by-case basis. Learn more below.
English language proficiency requirements
To meet the English language proficiency requirements of this course, you will need to demonstrate at least one of the following:
- bachelor degree from a recognised English-speaking country
- IELTS overall score of 6.5 (with no band score less than 6.0) or equivalent
- other evidence of English language proficiency (learn more about other ways to satisfy the requirements)
Admissions information
Learn more about Deakin courses and how we compare to other universities when it comes to the quality of our teaching and learning.
Not sure if you can get into Deakin postgraduate study? Postgraduate study doesn’t have to be a balancing act; we provide flexible course entry and exit options based on your desired career outcomes and the time you are able to commit to your study.
Pathways
Further study options:
Upon completion of the Master of Data Science, you could use the credit points you’ve earned to enter into further study, including:
S770 Master of Data Science (Professional)
Credit for prior learning - general
The University aims to provide students with as much credit as possible for approved prior study or informal learning.
You can 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.
Credit for prior learning - specific
Recognition of prior learning may be granted for relevant postgraduate studies, in accordance with standard University procedures.
Alternate exits
| Graduate Certificate of Data Analytics (S576) | |
| Graduate Certificate of Information Technology (S578) | |
| Graduate Diploma of Data Science (S677) |
Course duration
Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.
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.
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.
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.