C04372v2 Master of Data Science and Innovation
Award(s): Master of Data Science and Innovation (MDataScInn)CRICOS code: 084268K (Autumn, 2 years); 093052G (Spring, 2.5 years)
Commonwealth supported place?: No
Load credit points: 96
Course EFTSL: 2
Location: City campus
Overview
Career options
Course intended learning outcomes
Admission 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.
Work experience/industry placement is an important component of the course.
This course has been developed as a response to a global talent gap for people with data science knowledge, as identified and reported by the McKinsey Global Institute study (2011). The study predicted a shortfall by 2018 of nearly 200,000 data scientists and 1.5 million managers with the capability to make decisions using big data in the United States alone.
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
Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.
All applicants must satisfy the following requirements:
- bachelor degree, or higher qualification, in a relevant discipline, such as mathematical sciences, computer science, physics and astronomy, engineering, accounting, banking, finance and related fields, or economics and econometrics.
- minimum of three years professional/industry experience.
All applicants must provide:
- a personal statement, in which you explain (approx. 500 words) why you wish to study the course you are applying for, AND
- a CV, including details of paid and/or voluntary work or other experiences (eg. special interest groups) relevant to the course.
If you do not have a bachelor degree, or higher qualification, in a relevant discipline, you must also provide:
- a detailed explanation in your personal statement, of prior learning and demonstrated capability with quantitative data skills, key mathematical concepts and programming experience, AND
- detailed evidence in your CV, of prior learning and demonstrated capability with quantitative data skills, key mathematical concepts and programming experience.
The English proficiency requirement for international students or local applicants with international qualifications is: Academic IELTS: 6.5 overall with a writing score of 6.0; or TOEFL: paper based: 550-583 overall with TWE of 4.5, internet based: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64; or CAE: 176-184.
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.
Recognition of prior learning
A maximum of 12 credit points of unspecified exemptions may be considered under CBK91807 Options (Data Science and Innovation). 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. The maximum amount of credit allowed by UTS: Transdisciplinary Innovation for recognition of prior learning is one third of the total course credit point requirement for a postgraduate course.
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
For Autumn session intake, this course is offered on a two-year, full-time or four-year, part-time basis.
For Spring session intake, this course is offered on a two-and-a-half-year, full-time basis or four-to-five-year, part-time basis (dependent on the number of subjects undertaken each session).
Course structure
Student must complete 96 credit points (CP), comprising 56CP core and 40CP optional subjects. Optional subjects can be selected from specified data science related optional subjects and from across the University’s disciplines. Enrolment in subjects from other disciplines is dependent on approval from the Course Director and subject coordinator, 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 final iLab provides 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
| STM90984 Data Science Practices | 24cp | |
| STM90986 Innovation Labs | 24cp | |
| 36104 Data Visualisation and Narratives | 8cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Total | 96cp |
Course program
The following example shows a typical full-time program.
| Autumn commencing, full time | ||
| Year 1 | ||
| Autumn session | ||
| 36100 Data Science for Innovation | 8cp | |
| 36103 Statistical Thinking for Data Science | 8cp | |
| 36106 Data, Algorithms and Meaning | 8cp | |
| Spring session | ||
| Select 12 credit points from the following: | 12cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| 36102 iLab 1 | 12cp | |
| Year 2 | ||
| Autumn session | ||
| 36104 Data Visualisation and Narratives | 8cp | |
| Select 16 credit points from the following: | 16cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Spring session | ||
| Select 12 credit points from the following: | 12cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| 36105 iLab 2 | 12cp | |
| Autumn commencing, part time | ||
| Year 1 | ||
| Autumn session | ||
| 36100 Data Science for Innovation | 8cp | |
| 36103 Statistical Thinking for Data Science | 8cp | |
| Spring session | ||
| Select 12 credit points from the following: | 12cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Year 2 | ||
| Autumn session | ||
| Select 8 credit points from the following: | 8cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| 36106 Data, Algorithms and Meaning | 8cp | |
| Spring session | ||
| 36102 iLab 1 | 12cp | |
| Year 3 | ||
| Autumn session | ||
| 36104 Data Visualisation and Narratives | 8cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Spring session | ||
| 36105 iLab 2 | 12cp | |
| Year 4 | ||
| Autumn or Spring session | ||
| Select 12 credit points from the following: | 12cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Spring commencing, full time | ||
| Year 1 | ||
| Spring session | ||
| 36100 Data Science for Innovation | 8cp | |
| 36103 Statistical Thinking for Data Science | 8cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Year 2 | ||
| Autumn session | ||
| 36106 Data, Algorithms and Meaning | 8cp | |
| Select 12 credit points from the following: | 12cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Spring session | ||
| 36102 iLab 1 | 12cp | |
| Select 8 credit points from the following: | 8cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Year 3 | ||
| Autumn session | ||
| 36104 Data Visualisation and Narratives | 8cp | |
| Select 12 credit points from the following: | 12cp | |
| CBK91807 Options (Data Science and Innovation) | 40cp | |
| Spring session | ||
| 36105 iLab 2 | 12cp | |
Other information
For further information, contact the UTS Student Centre:
telephone 1300 ask UTS (1300 275 887)
or +61 2 9514 1222
Ask UTS