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Master of Data Science - ITM001

Course Code: ITM001
Course Version: 1
Location: UC - Canberra, Bruce
Faculty: Faculty of Science and Technology
Discipline(s): Academic Program Area - Technology
UAC Code: 880261
CRICOS Code: 099433A
English Language Requirements: Academic IELTS of 6.5 or equivalent, with no band score below 6.0

Introduction

The Masters in Data Science is aimed at addressing the societal fact that there is a dramatic growth in the scale and complexity of data that can be collected and analysed. Data and its analysis and modelling underpins all aspects of work and society including health care, sports, business practices, scientific discoveries and government policy. There is a need for capable and informed specialists who can make sense of that mass of data. Understanding effective and ethical ways of using vast amounts of data (big data) is a significant challenge to science and to society. Developing techniques for data analysis and decision making requires interdisciplinary research in many areas, including algorithmic reasoning, statistics, technology competence, data handling, big data analysis/modelling, pattern recognition/artificial intelligence, enterprise/cloud computing, data visualisation/communication, as well as privacy and security.
The University of Canberra's Masters in Data Science is designed to extend students to become society reference points and leaders in this rapidly growing area. Through a unique combination of interdisciplinary coursework, a good background in research methodology and the application to appropriate industry-based projects (work-integrated learning), this course will enable graduates to apply techniques and tools of data science to applications in health informatics, sports informatics, engineering, business intelligence, and finance. Graduates will be uniquely positioned to be leaders in this field in industry, the Government sector, and academia.

Enquiries

Prospective Domestic Students
Email study@canberra.edu.au or Phone 1800 UNI CAN (1800 864 226)
Prospective International Students
Email international@canberra.edu.au or Phone +61 2 6201 5342
Current and Commencing Students
In person, Student Centre Building 1 or Email Student.Centre@canberra.edu.au

Admission Requirements

An Australian bachelor degree in any field or equivalent.

Assumed Knowledge

Year 12 mathematics and functional knowledge of using computer systems.

Periods Course is Open to New Admissions

Year Location Domestic International
2020 UC - Canberra, Bruce Semester 1
Semester 2
Semester 1
Semester 2
2021 UC - Canberra, Bruce Semester 1
Semester 2
Semester 1
Semester 2
2022 UC - Canberra, Bruce Semester 1
Semester 2
Semester 1
Semester 2

Information on admission closing dates can be found here.

Course Requirements

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In addition to course requirements, in order to successfully complete your course you may need to meet the inherent requirements. Please refer to the inherent requirements statement applicable to your course

Awards

Award(s) Official Abbreviation
Master of Data Science MDS
Master of Data Science in Sports Analytics MDS SportAnalytics
Master of Data Science in Business Intelligence MDS BusIntelligence
Master of Data Science in AI and Computational Modelling MDS AICompModelling

Typical Study Pattern

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Please refer to the tab to view Course Requirements, check unit details and select Restricted Choice Units for the course. Please note not all units are offered in each teaching period.

Unit Delivery Modes

Delivery modes for units in this degree are yet to be confirmed. Please contact us for more details.

Information for Fee Paying Students

Year Domestic International
2020

$28,900

$33,000

The fees detailed above are the annual fee rates for the course. The annual rate is the fee that applies to standard full-time enrolment, which is 24 credit points. The final fee charged is based on the proportion of 24 credit points in which a student enrols. Students enrolled in a Commonwealth Support Place (CSP) are required to make a contribution towards the cost of their education, which is set by the Commonwealth Government. Information on Commonwealth Supported Places, HECS-HELP and how fees are calculated can be found here.


Course Learning Outcomes

Course Objectives Graduate Qualities
Demonstrate an advanced and fully operational set of specialised skills and knowledge in the underpinning mathematics and statistics to enable understanding of the appropriate tools to analyse data. UC graduates are professional: Employ up-to-date and relevant knowledge and skills.

UC graduates are global citizens: Make creative use of technology in their learning and professional lives.

UC graduates are lifelong learners: Evaluate and adopt new technology.
Have an advanced and integrated understanding of the common tools for programming, development and data management to enable basic database querying for data access, data processing, visualisation, and machine learning. UC graduates are professional: Employ up-to-date and relevant knowledge and skills.

UC graduates are global citizens: Make creative use of technology in their learning and professional lives.

UC graduates are lifelong learners: Evaluate and adopt new technology.
Interact with databases to query for relevant info, store data and deal with big data: mining massive amounts of information. UC graduates are professional: Employ up-to-date and relevant knowledge and skills.

UC graduates are global citizens: Make creative use of technology in their learning and professional lives.

UC graduates are lifelong learners: Evaluate and adopt new technology.
Demonstrate an advanced and integrated understanding of, and advanced abilities to utilise: Information technology and statistical tools in collecting, processing, analysing and extracting meaning from such diverse and extensive data sources. UC graduates are professional: Employ up-to-date and relevant knowledge and skills.

UC graduates are global citizens: Make creative use of technology in their learning and professional lives.

UC graduates are lifelong learners: Evaluate and adopt new technology.
Ability to transform raw data into features that are useful for predictive models. UC graduates are professional: Employ up-to-date and relevant knowledge and skills.

UC graduates are lifelong learners: Evaluate and adopt new technology.
Through appropriate research, be able to apply established theories to produce quantitative analyses and data-driven / evidence-based predictions on real-world data. UC graduates are professional: Employ up-to-date and relevant knowledge and skills; and use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems.

UC graduates are global citizens: Adopt an informed and balanced approach across professional and international boundaries.

UC graduates are lifelong learners: Evaluate and adopt new technology.
Advanced ability to apply data science solutions, data visualisation tools, data analysis skills and data mining tools to enable deep and effective analysis of data and information. UC graduates are professional: Employ up-to-date and relevant knowledge and skills; communicate effectively; and use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems.
Evaluate predictive models: how well do models predict a phenomenon? UC graduates are professional: Use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems.

UC graduates are lifelong learners: Adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas.
Visualise data for exploratory and confirmatory analysis and visualise model evaluations to assess business value / usefulness. UC graduates are professional: Communicate effectively.
Design, implement and evaluate data analysis projects that address contemporary and complex issues and effectively interpret and communicate skills and ideas to specialist and non-specialist audiences. UC graduates are professional: Use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems; and take pride in their professional and personal integrity.

UC graduates are lifelong learners: Reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development; and evaluate and adopt new technology.
Critically apply knowledge and skills relevant to Data Science to new and developing professional practice scenarios through working with industrial and professional partners. UC graduates are professional: Communicate effectively; use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems; work collaboratively as part of a team, negotiate, and resolve conflict; and take pride in their professional and personal integrity.

UC graduates are global citizens: Think globally about issues in their profession.

UC graduates are lifelong learners: Reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development; and adapt to complexity, ambiguity and change by being flexible and keen to engage with new ideas.
Ability to design and work within data driven projects that reinforce, enhance and evolve organisational and strategic goals. UC graduates are professional: Display initiative and drive, and use their organisational skills to plan and manage their workload; and take pride in their professional and personal integrity.

UC graduates are global citizens: Communicate effectively in diverse cultural and social settings; and behave ethically and sustainably in their professional and personal lives.

UC graduates are lifelong learners: Reflect on their own practice, updating and adapting their knowledge and skills for continual professional and academic development.

Course Duration

Standard 2 years full-time, or part-time equivalent. Maximum duration - 6 years.

Government Support

Year CSP Places Available Eligible for Student
Income Support (Centrelink)
2020 NO NO

Offerings

Enrolment Numbers

View the number of student enrolments for the previous full year. Please note that course numbers are indicative only and in no way reflect individual class sizes.

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