Master of Data Science
2024 Deakin University Handbook
| Year | 2024 course information |
|---|---|
| Award granted | Master of Data Science |
| Deakin course code | S777 |
| Faculty | Faculty of Science, Engineering and Built Environment |
| Campus | Offered at Burwood (Melbourne) |
| Online | Yes |
| Duration | Depending on your professional experience and previous qualifications, your course will be:
|
| Course Map - enrolment planning tool | The course map for new students commencing from Trimester 1 2024 The course map for new students commencing from Trimester 2 2024 The course map for new students commencing from Trimester 3 2024 Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central. |
| CRICOS course code | 099225J Burwood (Melbourne) |
| 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
- Indicative student workload
- Professional recognition
- Career opportunities
- Participation requirements
- Mandatory student checks
- Pathways
- Alternative exits
- Fees and charges
- Course Learning Outcomes
- Course rules
- Course structure
- Work experience
- Other learning experiences
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
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)
Alternative exits
| Graduate Certificate of Data Analytics (S576) | |
| Graduate Certificate of Information Technology (S578) | |
| Graduate Diploma of Data Science (S677) |
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, and 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.
Use the Fee estimator to see course and unit fees applicable to your course and type of place. For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current students website.
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.
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.
Other course information
Course duration
Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.
Further information
Student Central can help you with course planning, choosing the right units and explaining course rules and requirements.
- Contact Student Central
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.