University of Technology Sydney

C04370v1 Master of Data Science and Innovation

Award(s): Master of Data Science and Innovation (MDataScInn)
CRICOS code: 109317G
Commonwealth supported place?: No
Load credit points: 48
Course EFTSL: 1
Location: City campus

Notes

C04370 is a 1-year full-time or 2-year part-time course. If you are not qualified, UTS also offers the same course with 2-year and 1.5-year duration entries C04372


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 (48cp) is a world-leading program of study in analytics and data science, specifically designed for professionals who have work experience in Data Analytics or IT but wish to enhance their career prospects in Data Science.

Taking a transdisciplinary approach to learning, the course leverages students' rich industry experience, utilises a range of perspectives from diverse fields, and integrates them with hands-on training in innovative approaches and creative thinking towards data problems, equipping graduates with advanced and transferrable skills to leverage the latest data science approaches and best practices in tackling complex problems across a broad range of industries, sectors, and organisations.

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. The course is delivered in a range of modes, including contemporary online and face-to-face learning experiences in UTS's leading-edge facilities.

Career options

Data Science is one of the most exciting areas to study and the career prospects are enormous, with a variety of career options – data scientist, data engineer, business analyst, data analyst, data manager, analytics consultant, data journalist, data-driven policy expert, and advertising and marketing specialist, 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.

In addition, applicants who have completed a UTS recognised bachelor's degree, or an equivalent must also meet the following criteria:

1. 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 one of the following fields.
    • 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

AND

2. The applicant must have a minimum of two-year full-time, or equivalent part-time, work experience within the last five years in one of the following ANZSCO listed occupations.

  • 2241 Actuaries, Mathematicians and Statisticians
  • 2243 Economists
  • 2244 Intelligence and Policy Analysts
  • 225112 Market Research Analyst
  • 225115 Digital Marketing Analyst
  • 26 ICT Professionals

To support their application these applicants 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.

If applicants have completed a higher qualification than a UTS recognised bachelor's degree, the above GPA requirement may be waived.

Applicants with an appropriate degree and a minimum of two-year full-time relevant work experience who do not fully meet either of these criteria may still be considered for an offer.

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 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

No recognition of prior learning is considered for this course.

Course duration and attendance

This course is offered on a one-year, full-time or two-year, part-time basis.

Course structure

Students must complete 48 credit points made up of 32 credit points of core subjects and 16 credit points of electives.

Course completion requirements

STM91736 Data Science Accelerated core subjects 32cp
CBK92163 Electives 16cp
Total 48cp

Course program

The following example shows a typical full-time program.

1 year, Autumn commencing, full time
Year 1
Autumn session
36106 Machine Learning Algorithms and Applications   8cp
36103 Statistical Thinking for Data Science   8cp
Select 8 credit points from the following:   8cp
CBK91916 Electives 20cp  
Spring session
94692 Data Science Practice   8cp
Select 8 credit points from the following:   8cp
36118 Applied Natural Language Processing 8cp  
94693 Big Data Engineering 8cp  
94691 Deep Learning 8cp  
Select 8 credit points from the following:   8cp
CBK91916 Electives 20cp  
1 year, Spring commencing, full time
Year 1
Spring session
94692 Data Science Practice   8cp
36106 Machine Learning Algorithms and Applications   8cp
36103 Statistical Thinking for Data Science   8cp
Year 2
Autumn session
Select 8 credit points from the following:   8cp
36118 Applied Natural Language Processing 8cp  
94693 Big Data Engineering 8cp  
94691 Deep Learning 8cp  
Select 16 credit points from the following:   16cp
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