C04372v4 Master of Data Science and Innovation
Award(s): Master of Data Science and Innovation (MDataScInn)CRICOS code: 084268K (Autumn, 2 years); 084268K (Spring, 2 years)
Commonwealth supported place?: No
Load credit points: 96
Course EFTSL: 2
Location: City campus
Notes
Depending on qualifications and work experience, C04372 can be taken with either 2-year duration or 1.5-year duration. If you have relevant work experience, you may qualify for the accelerated version of the course with 1-year full time duration C04370.
Overview
Career options
Course intended learning outcomes
Admission requirements
Inherent requirements
Recognition of prior learning
Course duration and attendance
Course structure
Course completion requirements
Course program
Other information
Overview
The Master of Data Science and Innovation is a world-leading program of study in analytics and data science.
Taking a transdisciplinary approach, the course utilises a range of perspectives from diverse fields and integrates them with industry experiences, real-world projects and self-directed study, equipping graduates with an understanding of the potential of analytics to transform practice. The course is delivered in a range of modes, including contemporary online and face-to-face learning experiences in UTS's leading-edge facilities.
Industry partnerships and engagement are a core part of the course. The course curriculum and subjects are co- designed and developed by UTS academic data experts and industry partners, and regularly reviewed and updated to keep up with the current market needs and latest data science trends. During the course study, students have abundant opportunities working on real world data sets/projects.
The dramatic growth of data in every conceivable industry, from oceanography to market research, presents another major driving force in generating unprecedented global demand for data science skills.
Career options
The course prepares students to participate in a variety of emerging careers with the growth of data science – data scientist, data engineer, data griot, data analyst, data artist, data journalist and data-driven policy expert, to name a few. While other offerings also provide the basis for these careers, this unique transdisciplinary course is the first of its kind in Australia where creativity and innovation are integral components, producing industry-ready graduates with strong technical, creative thinking and data ethics skills.
Course intended learning outcomes
| 1.1 | Identify and represent the human and technical elements and processes within complex systems and organise them within frameworks of relationships |
| 1.2 | Explore and test models and generalisations for describing the behaviour of sociotechnical systems and selecting data sources, taking into account the needs and values of different contexts and stakeholders |
| 1.3 | Analyse the value of different models, established assumptions and generalisations, about the behaviour of particular systems, for making predictions and informing data discovery investigations |
| 1.4 | Use transdisciplinary approaches to seeing and doing to uncover underrepresented, or misrepresented, elements of a system |
| 2.1 | Critique contemporary trends and theoretical frameworks in data science for relevance to one's own practice |
| 2.2 | Explore, analyse, manipulate, interpret and visualise data using data science techniques, software and technologies to make sense of data rich environments |
| 2.3 | Understand and deal critically and openly with the uncertainty, ambiguity and complexity associated with people, systems and data |
| 2.4 | Apply and assess data science concepts, theories, practices and tools for designing and managing data discovery investigations in professional environments that draw upon diverse data sources, including efforts to shed light on underrepresented components |
| 3.1 | Explore, interrogate, generate, apply, test and evaluate problem-solving strategies to extract economic, business, social, strategic or other value from data |
| 3.2 | Critically examine the perceived value of data analytics outcomes and clearly articulate implications for different stakeholders and organisations |
| 3.3 | Develop a collaborative and team-oriented mindset to harness value for stakeholders to produce innovative solutions to challenges |
| 4.1 | Collaborate to develop and refine multimodal communication skills needed to successfully work in data science teams |
| 4.2 | Explore and craft interpretative narratives that engage key audiences with data analytics and potential significance for action, at a societal, industrial, organisational, group or individual levels |
| 4.3 | Develop, test, justify and deliver data project propositions, methodologies, analytics outcomes and recommendations for informing decision-making, both to specialist and non-specialist audiences |
| 5.1 | Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts |
| 5.2 | Interrogate and justify ethical responsibilities related to data selection, access, analysis and governance to create a framework for practice |
| 5.3 | Take a leadership role in promoting positive change in data science contexts, recognising individual, organisational and community issues |
Admission requirements
To be eligible for admission to this course, applicants must meet the following criteria.
Applicants must have one of the following:
- Completed Australian bachelor's degree or higher qualification, or overseas equivalent, in any discipline with a minimum GPA of 4/7
OR
- Completed Australian bachelor's degree or higher qualification, or overseas equivalent, in any discipline AND A minimum of 2 years full-time, or equivalent part-time, professional experience in a relevant professional occupation (listed below)
OR
- Completed Australian bachelor's degree or higher qualification, or overseas equivalent, in a relevant discipline (listed below)
Successful applicants who also meet the following criteria may be eligible for 24cp of recognition of prior learning, reducing the course duration from 2 years to 1.5 years:
- a minimum GPA of 4/7 and a minimum of 2 years full time, or equivalent part-time, professional experience in a relevant professional occupation (listed below)
OR
- a minimum GPA of 4/7 and an academic qualification in a relevant discipline (listed below)
OR
- an academic qualification in a relevant discipline (listed below) and a minimum of 2 years full time, or equivalent part-time, professional experience in a relevant professional occupation (listed below)
Applicants who do not meet the criteria above should consider applying for C06124 Graduate Diploma in Data Science and Innovation or C11274 Graduate Certificate in Data Science and Innovation.
Relevant disciplines:
Natural and Physical Sciences, Information Technology, Engineering and related technologies, Accounting, Business and Management, Sales and Marketing, Banking, Finance and related fields, Economics and Econometrics
Relevant professional occupations (ANZCO classification):
2241 Mathematical Science Professionals, 2243 Economists, 2244 Intelligence and Policy Analysts, 225112 Market Research Analyst, 225115 Digital Marketing Analyst, 26 ICT Professionals
Supporting documentation to be submitted with the application
For applicants who need to demonstrate work experience:
- Curriculum Vitae AND Statement of service in one of the following formats:
- A 'Statement of Service' provided by the employer
- A completed 'UTS statement of service’ signed by the employer
- A statutory declaration confirming work experience (for Australian Residents only)
- An official letter from the applicant’s accountant or solicitor on their company letterhead confirming the applicant’s work experience or engagement with the business, duration of operations, and the nature of the business
- A business certificate of registration in original language and English (e.g. provision of ASIC documentation or ABN or similar documentation for Australian Businesses)
The English proficiency requirement for international students or local applicants with international qualifications is: IELTS Academic: 6.5 overall with a writing score of 6.0; or TOEFL iBT: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64 with a writing score of 50; or C1A/C2P: 176-184 with a writing score of 169.
Eligibility for admission does not guarantee offer of a place.
International students
Visa requirement: To obtain a student visa to study in Australia, international students must enrol full time and on campus. Australian student visa regulations also require international students studying on student visas to complete the course within the standard full-time duration. Students can extend their courses only in exceptional circumstances.
Inherent requirements
Inherent requirements are academic and non-academic requirements that are essential to the successful completion of a course. For more information about inherent requirements and where prospective and current students can get assistance and advice regarding these, see the UTS Inherent requirements page.
Prospective and current students should carefully read the Inherent Requirements Statement below and consider whether they might experience challenges in successfully completing this course.
UTS will make reasonable adjustments to teaching and learning, assessment, professional experiences, course related work experience and other course activities to facilitate maximum participation by students with disabilities, carer responsibilities, and religious or cultural obligations in their courses.
For course specific information see the TD School Inherent (Essential) Requirements Statement.
Recognition of prior learning
A maximum of 48cp exemptions may be granted for the course with a maximum of 24cp being unspecified subjects. Exemptions are granted only on the basis of prior postgraduate study at an Australian university, or at a recognised overseas institution deemed to be equivalent to an Australian university.
To be eligible for recognition of prior learning, the subject being considered for prior study must have been completed within five years of commencing the course. Recognition of study completed before this period is not considered.
Course duration and attendance
This course is offered on a two-year, full-time or four-year, part-time basis.
Course structure
Students must complete 96 credit points (CP), comprising 40CP core subjects and 56CP elective subjects. Elective subjects can be selected from data science-related subjects and from across the University’s disciplines. Enrolment in subjects from other faculties may require submission of an e-request and be dependent on approval from the subject coordinator of the host faculty, and usually requires demonstrated ability to meet pre-requisites. This flexible course structure enables students to pursue their own particular interests and career aspirations.
Students who have completed certain components of this course may qualify for a Graduate Certificate in Data Science and Innovation (C11274) or Graduate Diploma in Data Science and Innovation (C06124).
Industrial training/professional practice
The iLabs and internship projects provide the opportunity for students to design investigations utilising contemporary data discovery techniques and large, complex, multi-structure data sets. The study can focus on the student's current work environment, or industry placements can be negotiated in a discipline of interest.
Course completion requirements
| STM91950 Data Science core subjects | 40cp | |
| CBK92377 Electives | 56cp | |
| Total | 96cp |
Course program
The Master of Data Science and Innovation has a comprehensive and flexible course structure. Students are able to study different subject combinations to complete the course. The following study plan indicates a typical structure of a full-time program.
Note:
- you must plan your study on at least an annual basis and choose specific subjects that are suitable for your academic background and learning objectives
- subject availability is subject to annual review. Not all subjects listed on the program are offered every year or every semester. Please refer to UTS Timetable Planner for confirmation of subject availability.
- we have mixed subjects of different credit points. It is the responsibility of students to choose proper subjects to complete the course with 96 credit points. Completing the course with more than 96 credit points are allowed by submitting an e-request.
| 1.5 years, Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 36100 Data Science for Innovation | 8cp | |
| 36104 Data Visualisation and Narratives | 8cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| Spring session | ||
| Select 8 credit points from the following: | 8cp | |
| CBK91916 Electives | 20cp | |
| 36103 Statistical Thinking for Data Science | 8cp | |
| 36106 Machine Learning Algorithms and Applications | 8cp | |
| Year 2 | ||
| Autumn session | ||
| 36105 iLab: Capstone Project | 12cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| 1.5 years, Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| 36103 Statistical Thinking for Data Science | 8cp | |
| 36100 Data Science for Innovation | 8cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| Year 2 | ||
| Autumn session | ||
| 36106 Machine Learning Algorithms and Applications | 8cp | |
| 36104 Data Visualisation and Narratives | 8cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| Spring session | ||
| 36105 iLab: Capstone Project | 12cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| 2 years, Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 36100 Data Science for Innovation | 8cp | |
| 36104 Data Visualisation and Narratives | 8cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| Spring session | ||
| 36106 Machine Learning Algorithms and Applications | 8cp | |
| 36103 Statistical Thinking for Data Science | 8cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| Year 2 | ||
| Autumn session | ||
| Select 8 credit points from the following: | 8cp | |
| CBK91916 Electives | 20cp | |
| Select 16 credit points from the following: | 16cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| Spring session | ||
| 36105 iLab: Capstone Project | 12cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| 2 years, Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| Select 8 credit points from the following: | 8cp | |
| CBK91916 Electives | 20cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| 36100 Data Science for Innovation | 8cp | |
| Year 2 | ||
| Autumn session | ||
| 36104 Data Visualisation and Narratives | 8cp | |
| 36106 Machine Learning Algorithms and Applications | 8cp | |
| 36103 Statistical Thinking for Data Science | 8cp | |
| Spring session | ||
| Select 8 credit points from the following: | 8cp | |
| CBK91916 Electives | 20cp | |
| Select 16 credit points from the following: | 16cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
| Year 3 | ||
| Autumn session | ||
| 36105 iLab: Capstone Project | 12cp | |
| Select 6 credit points from the following: | 6cp | |
| CBK91916 Electives | 20cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91915 Options (Data Science and Innovation) MDataScInn | 32cp | |
Other information
For further information, contact the UTS Student Centre:
telephone 1300 ask UTS (1300 275 887)
or +61 2 9514 1222
Ask UTS