Master of Data Science
2019 Deakin University Handbook
| Year | 2019 course information |
|---|---|
| Award granted | Master of Data Science |
| Course Map | If you started your course before 2019, please refer to the plan your study page or contact a Student Adviser |
| Campus | Offered at Burwood (Melbourne) |
| Cloud Campus | Yes |
| Duration | Depending on your professional experience and previous qualifications, your course will be:
|
| CRICOS course code | 099225J Burwood (Melbourne) |
| Deakin course code | S777 |
| 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 9. |
Course sub-headings
- Course overview
- Indicative student workload
- 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
Deakin’s Master of Data Science prepares students for professional employment across all sectors. 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. Become a data analytics specialist capable of using data to learn insights and support decision making.
Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions.
Throughout your studies you’ll learn 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’ll 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.
Units in the course may include assessment hurdle requirements.
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.
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. Click here for more information.
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) |
Fees and charges
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 the Current students fees 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 7 June 2018
Course rules
To complete the Master of Data Science, you will complete 8, 12 or 16 credit points, depending on your prior experience.
The course is structured in three 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. Click here for more information.
Course structure
Core
Mandatory unit for all entry levels
| STP050 | Academic Integrity (0 credit points) |
Part A: Fundamental Data Analytics Studies
| MIS770 | Foundation Skills in Data Analysis |
| SIT718 | Real World Analytics |
| SIT719 | Security and Privacy Issues in Analytics |
| SIT740 | Research and Development in Information Technology |
Part B: Introductory Data Science Studies
| SIT720 | Machine Learning |
| SIT741 | Statistical Data Analysis |
| SIT742 | Modern Data Science |
| MIS771 | Descriptive Analytics and Visualisation |
Part C: Mastery Data Science Studies
| SIT743 | Multivariate and Categorical Data Analysis |
| SIT744 | Practical Machine Learning for Data Science |
| SIT764 | Project Analysis and Design ~ |
| SIT782 | Project Delivery ~ |
~ Note: Students are expected 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.
Electives should be selected from one of the following combinations:
Four (4) credit points of project/thesis/placement units from the list below:
| SIT790 | Major Thesis (4 cp), or |
| SIT791 | Professional Practice (4 cp)*, or |
| SIT792 | Minor Thesis (2 cp), and |
2 additional level 7 SIT/MIS-coded elective units or
| SIT709 | Internship - Information Technology (1 cp)*, and |
3 additional level 7 SIT/MIS-coded elective units
*Students undertaking this unit must have successfully completed STP710 Introduction to Work Placements (0 credit point)
Work experience
You will have an opportunity to undertake a placement as part of your course.
Other course information
Course duration - additional information
Course duration may be affected by delays in completing course requirements, such as accessing or completing work placements.
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.