University of Technology Sydney

C04372v3 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

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.

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

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.

The academic qualification used to support the application for admission must:

  • have been completed with a GPA of at least 4.0 on a 7.00 GPA scale, and
  • be in a relevant discipline, such as information technology, mathematical sciences, physics and astronomy, engineering, accounting, business and management, banking, finance and related fields, or economics and econometrics.

If the applicant's academic qualification is not in the above listed disciplines, but they do have at least two-year full time, or part-time equivalent, work experience in data analytics, database management or programming related fields, they may be considered for admission. To support their application they must provide:

  • a C.V. outlining work experience and education, as well as other relevant evidence and information, and
  • an official Statement of Service, from the employer, confirming the dates of employment, and a description of the position held within the organisation.

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 32cp exemptions may be granted for the course with a maximum of 12cp 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

Student must complete 96 credit points (CP), comprising 44CP core subjects, 32CP specified data science related optional subjects and 20CP elective subjects. Elective subjects can be selected from data science related 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 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

STM91478 Data Science Core Subjects 44cp
CBK91915 Options (Data Science and Innovation) MDataScInn 32cp
CBK91916 Electives 20cp
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
36106 Machine Learning Algorithms and Applications   8cp
Select 6 credit points from the following:   6cp
CBK91916 Electives 20cp  
Spring session
94692 Data Science Practice   8cp
36109 Data and Decision Making   8cp
36103 Statistical Thinking for Data Science   8cp
Year 2
Autumn session
36104 Data Visualisation and Narratives   8cp
94693 Big Data Engineering   8cp
Select 8 credit points from the following:   8cp
CBK91916 Electives 20cp  
Spring session
36105 iLab 2   12cp
36101 Leading Data Science Initiatives   8cp
Select 6 credit points from the following:   6cp
CBK91916 Electives 20cp  
Spring commencing, full time
Year 1
Spring session
36109 Data and Decision Making   8cp
36103 Statistical Thinking for Data Science   8cp
94692 Data Science Practice   8cp
Year 2
Autumn session
36106 Machine Learning Algorithms and Applications   8cp
36104 Data Visualisation and Narratives   8cp
36100 Data Science for Innovation   8cp
Spring session
36105 iLab 2   12cp
36101 Leading Data Science Initiatives   8cp
Select 6 credit points from the following:   6cp
CBK91916 Electives 20cp  
Year 3
Autumn session
94691 Deep Learning   8cp
Select 14 credit points from the following:   14cp
CBK91916 Electives 20cp  

Other information

For further information, contact the UTS Student Centre:

telephone 1300 ask UTS (1300 275 887)
or +61 2 9514 1222
Ask UTS