C04372v1 Master of Data Science and Innovation
Award(s): Master of Data Science and Innovation (MDataScInn)UAC code: 940680 (Autumn session, Spring session)
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
Credit recognition
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 griot, data analyst, data artist, data journalist, mobile behaviour analyst, data-driven policy expert, advertising insight and online community manager, to name a few. While other offerings also provide the basis for these careers, this course provides an additional level of expertise, targeting professionals who have the desire to lead teams and organisations at the chief executive level.
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 under-represented 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 for working 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.
Previous qualifications should be in one of the following areas: mathematical sciences; computer science; physics and astronomy; engineering; accounting; banking, finance and related fields; economics and econometrics. If academic qualifications are not in these fields, the applicant must provide evidence of prior learning and demonstrated capability with quantitative data skills, key mathematical concepts and programming experience. Applicants who hold other academic qualifications may be considered on the basis of general and professional qualifications that demonstrate their potential for the course.
A minimum of three years professional/industry experience or a demonstrated equivalent is required.
A personal statement is also required for all applications.
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.
Credit recognition
Students who have previously undertaken postgraduate study or completed an undergraduate degree at a university or other recognised tertiary education institutions may be eligible for credit recognition where subjects previously completed are found to be equivalent in regard to learning outcomes, content, volume of learning and assessment approaches. A maximum of 12 credit points (two electives) can be submitted for consideration. No automatic credit is given and applications must be made at the time of seeking admission into this course.
To be eligible for credit recognition, the subject being considered for prior study must have been completed within two years of commencing the course. Recognition of study completed before this period is not considered.
No core subjects are considered for credit recognition.
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
Students must complete 96 credit points in total, comprising 48 credit points of core subjects, 24 credit points of laboratories and 24 credit points of electives.
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 | |
| STM90985 Data Science Leadership | 24cp | |
| STM90986 Innovation Labs | 24cp | |
| CBK91256 Electives (Data Science and Innovation) | 24cp | |
| Total | 96cp |
Course program
The following example shows a typical full-time program.
| 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 | |
| CBK91256 Electives (Data Science and Innovation) | 24cp | |
| 36102 iLab 1 | 12cp | |
| Year 2 | ||
| Autumn session | ||
| 36101 Leading Data Science Initiatives | 8cp | |
| 36104 Data Visualisation and Narratives | 8cp | |
| 36109 Data and Decision Making | 8cp | |
| Spring session | ||
| Select 12 credit points from the following: | 12cp | |
| CBK91256 Electives (Data Science and Innovation) | 24cp | |
| 36105 iLab 2 | 12cp | |
| 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 | |
| CBK91256 Electives (Data Science and Innovation) | 24cp | |
| Year 2 | ||
| Autumn session | ||
| 36101 Leading Data Science Initiatives | 8cp | |
| 36106 Data, Algorithms and Meaning | 8cp | |
| Spring session | ||
| 36102 iLab 1 | 12cp | |
| Year 3 | ||
| Autumn session | ||
| 36104 Data Visualisation and Narratives | 8cp | |
| 36109 Data and Decision Making | 8cp | |
| Spring session | ||
| 36105 iLab 2 | 12cp | |
| Year 4 | ||
| Autumn or Spring session | ||
| Select 12 credit points from the following: | 12cp | |
| CBK91256 Electives (Data Science and Innovation) | 24cp | |
Other information
For further information, contact the UTS Student Centre:
telephone 1300 ask UTS (1300 275 887)
or +61 2 9514 1222
Ask UTS
