Master of Data Science (Professional)

2023 Deakin University Handbook

Note: You are seeing the 2023 view of this course information. These details may no longer be current. [Go to the current version]

Year	2023 course information
Award granted	Master of Data Science (Professional)
Course Map	The course map for new students commencing from Trimester 1 2023. Professional Studies: Professional Practice Professional Studies: Team Project Professional Studies: Research Training, Project and Electives Professional Studies: Research Project The course map for new students commencing from Trimester 2 2023. Professional Studies: Professional Practice Professional Studies: Team Project Professional Studies: Research Training, Project and Electives Professional Studies: Research Project The course map for new students commencing from Trimester 3 2023. Professional Studies: Professional Practice Professional Studies: Team Project Professional Studies: Research Training, Project and Electives Professional Studies: Research Project Course maps for commencement in previous years are available on the Course Maps webpage or please contact a Student Adviser in Student Central.
Campus	Offered at Burwood (Melbourne)
Online	Yes
Duration	2 years full-time or part-time equivalent
CRICOS course code	107030E Burwood (Melbourne)
Deakin course code	S770
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

The sheer volume and complexity of data at the fingertips of business gives rise to challenges that must be solved by tomorrow's graduates. Modern organisations are placing increasing emphasis on the use of data to inform day-to-day operations and long-term strategic decisions. You will explore the various origins of data and the 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. You will gain the technical skills to harness the power of data through artificial intelligence and machine learning to develop innovative solutions to the challenges being faced by industry and governments. With a growing demand for data specialists in every sector, you will be equipped with the skills to optimise performance and add a competitive advantage.

Want to take your career to the next level with specialised study?

The Master of Data Science (Professional) is designed to extend the specialised skills obtained in the Master of Data Science by providing you with the opportunity to undertake a period of industry-based learning or a research project under the supervision of our internationally-recognised staff.

You will also have the opportunity to hone your skills is a specialisation of your choosing, with options ranging from cyber security to blockchain and software development, networking and cloud technologies to AI and more.

You will develop expert knowledge of the technical aspects of data science as well as in-depth skills in your chosen area of specialisation.

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

In fiercely competitive markets where businesses are constantly striving to increase profit, reduce costs and provide exceptional customer value, the requirement for skilled data professionals is growing at a rapid pace. 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

Students commencing in Trimester 3 may be required to complete units in subsequent Trimester 3 teaching periods depending on their choice of specialisation and/or Professional Studies option.

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.

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 (Professional) are as follows:

Graduate Certificate of Information Technology (followed by a 16 credit point Master of Data Science (Professional))
Graduate Certificate of Data Analytics (followed by a 12 credit point Master of Data Science (Professional))
Graduate Diploma of Data Science (followed by a 8 credit point Master of Data Science (Professional))
Master of Data Science (followed by a 4 credit point Master of Data Science (Professional))

Pathway options will depend on your professional experience and previous qualifications.

Alternate exits

Graduate Certificate of Data Analytics (S576)
Graduate Diploma of Data Science (S677)
Master of Data Analytics (S777)

Fees and charges

Fees and charges vary depending on your course, the type of fee place you hold, your commencement year, the units you choose 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. Further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods is available on our 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. Have a broad appreciation of advanced topics within the IT domain through engagement with research or specialist studies.
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 (Professional), students must attain 16 credit points. Full time students’ study 4 credit points per trimester, and usually undertake two trimesters each year.

The course is structured in three parts:

Part A. Core Data Science Studies (8 credit points),
Part B. Specialisation (4 credit points) or course electives (4 credit points), and
Part C. Professional Studies (4 credit points)

The three parts comprise the following:

eight (8) credit points of core units,
four (4) credit point specialisation, or four (4) credit points of course electives (level 7 SIT or MIS coded units) (excluding SIT771, SIT772, SIT773 and SIT774)
four (4) credit points of professional units
completion of STP050 Academic Integrity (0-credit-point compulsory unit)

Students commencing in Trimester 3 may be required to complete units in subsequent Trimester 3 teaching periods depending on their choice of specialisation and/or Professional Studies option.

Specialisations

Refer to the details of each specialisation for availability.

Students commencing in Trimester 3 may be required to complete units in subsequent Trimester 3 teaching periods depending on their choice of specialisation.

Course structure

Core

Mandatory unit for all entry levels

STP050

Academic Integrity (0 credit points)

Part A: Core Data Science Studies

SIT718

Real World Analytics

SIT731

Data Wrangling

SIT787

Mathematics for Artificial Intelligence

SIT720

Machine Learning

SIT741

Statistical Data Analysis

SIT742

Modern Data Science

SIT743

Bayesian Learning and Graphical Models

SIT744

Deep Learning

Part B: Specialisation or course electives

Four (4) core units from a chosen specialisation (four credit points),

Four (4) course electives (level 7 SIT or MIS coded units)^#

Part C: Professional Studies

SIT753

Professional Practice in Information Technology

SIT764

Team Project (A) - Project Management and Practices ~

SIT782

Team Project (B) - Execution and Delivery

One (1) level 7 SIT elective (1 credit point)

STP710

Career Tools for Employability (0 credit points)

SIT753

Professional Practice in Information Technology

SIT791

Professional Practice (2 credit points)*

One (1) level 7 SIT elective (1 credit point)

SIT723

Research Training and Project (2 credit points)@

SIT746

Research Project (Advanced) (2 credit points)^@

SIT753

Professional Practice in Information Technology

SIT723

Research Training and Project (2 credit points)@

One (1) level 7 SIT elective (1 credit point)

*Students undertaking this unit must have successfully completed STP710 Career Tools for Employability (0-credit point unit)

~ 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.

# excluding SIT771, SIT772, SIT773, SIT774

^ Entry to SIT746 is subject to specific unit entry requirements.

^@Students commencing in Trimester 3 wishing to undertake a Research Project for their PART C: Professional Studies may be required to complete units in subsequent Trimester 3 teaching periods.

Work experience

You will have an opportunity to undertake a placement as part of your course.

Details of specialisations

Cyber Security

Campuses

Burwood (Melbourne), Online

Unit set code

SP-S000028

Overview

Develop skills in securing data, communications and infrastructure as well as investigating, analysing and providing solutions to computer crime. Students gain an understanding of problem solving, communication and technical capabilities related to Information Technology Security and the legal, regulatory and ethical contexts in which these skills are used. The security units provide a solid foundation in areas including information security, internet and network security, access controls and firewalls. In conjunction with work experience, the units prepare students towards certification as a Certified Information Systems Security Professional on completion of the CISSP exam administered by The International Information Systems Security Certification Consortium (ISC)2.

Units

SIT716

Computer Networks and Security

SIT704

Ethical Hacking

And two (2) units from the following:

SIT703

Computer Forensics and Investigations

SIT735

Application and Communication Protocol Security

SIT736

Identity, Access Management and Physical Security

SIT738

Secure Coding

SIT763

Cyber Security Management

S770 Master of Data Science (Professional) students wishing to undertake this specialisation may be required to complete units in Trimester 3, depending on the Professional Studies option they choose.

AI and Computer Vision

Campuses

Online

Unit set code

SP-S000093

Overview

Artificial intelligence (AI) is fast becoming the cornerstone to technological progress across industries and is estimated to contribute up to $15.7 trillion to the global economy by 2030. This specialisation will prepare you with knowledge and skills in the application of computer vision and speech processing; human aligned AI; and the design, development and deployment of AI solutions for enhanced business operations.

Units

SIT788

Engineering AI Solutions

SIT789

Applications of Computer Vision and Speech Processing

SIT796

Reinforcement Learning

SIT799

Human Aligned Artificial Intelligence

Analytics in Internet of Things

Campuses

Burwood (Melbourne), Online

Unit set code

SP-S000094

Overview

There is a growing demand for Internet of Things (IoT) related skillsets in the IT job market. Many organisations are starting to realise the enormous potential of IoT in their business, however, they also acknowledge the major shortage of experts who are skilled in this domain. This specialisation will help equip you with the ability to design and deploy safe, secure and successful IoT solutions to meet business needs.

Units

SIT725

Applied Software Engineering

SIT729

Software Architecture and Scalability for Internet of Things

SIT730

Embedded Systems Development

Plus one unit in:

SIT722

Software Deployment and Operation

SIT732

Developing Secure Internet of Things Applications

Blockchain and Software Development

Campuses

Burwood (Melbourne)

Unit set code

SP-S000092

Overview

This specialisation introduces you to state-of-the-art blockchain technologies and allows you to explore various software platforms and tools used for large-scale data analysis. You will learn how to build software solutions using modern software engineering techniques and have the opportunity to choose to hone your studies in the design and development of cloud applications or blockchain technologies depending on your area of interest.