University of Technology Sydney

C06124v3 Graduate Diploma in Data Science and Innovation

Award(s): Graduate Diploma in Data Science and Innovation (GradDipDataScInn)
CRICOS code: 084267M
Commonwealth supported place?: No
Load credit points: 48
Course EFTSL: 1
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
Other information

Overview

The Graduate Diploma in Data Science and Innovation is a part of nested qualifications for the Master of Data Science and Innovation (C04372), a world-leading program of study in analytics and data science.

Taking a transdisciplinary approach, the master's 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.

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. 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 investigation
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
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
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
5.1 Engage in active, reflective practice that supports flexible navigation of assumptions, alternatives and uncertainty in professional data science contexts
5.2 Take a leadership role in promoting positive change in data science contexts, recognising individual, organisational and community issues, including indigenous worldviews and cultures.

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 16cp 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

Students generally complete the required credit points in one year of full-time or two years of part-time study.

Course structure

Students must complete 48 credit points in total, including 16 credit points of electives.

Course completion requirements

STM91479 Options MDSI 32cp
CBK91918 Electives 16cp
Total 48cp

Other information

For further information, contact the UTS Student Centre:

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