C04370v2 Executive Master of Data Science and Innovation
Award(s): Executive Master of Data Science and Innovation (ExecMDataScInn)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
This course is designed for professionals seeking to advance their careers by leading in data science and driving innovation within organisations. It offers not only technical proficiency but also the strategic and leadership skills required to excel at the executive level. Through a unique transdisciplinary approach, the program combines cutting-edge data science with industry-relevant experience, preparing students to lead teams and transform business practices.
Students will gain expertise in advanced data analytics, project management, and strategic implementation, tailored to today’s market demands. The curriculum integrates online and in-person sessions at UTS’s state-of-the-art facilities and features real-world data projects with authentic assessments, ensuring learning is both relevant and immediately applicable.
As data grows exponentially across industries, this course equips students to meet the global demand for executive data professionals who can harness analytics to drive business success.
Career options
Graduates of this executive-level data science course will be equipped to take on leadership roles in data-driven industries. The program prepares students for senior positions in data science, analytics, business intelligence, and innovation management, where they will lead teams, drive strategic decision-making, and foster innovation through data. Students will be uniquely positioned to excel in industries ranging from technology to marketing, policy, and beyond, combining technical expertise with creative problem-solving and ethical leadership.
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
To be eligible for admission to this course, applicants must meet the following criteria.
Applicants must have one of the following:
- Completed Australian bachelor's degree, or overseas equivalent, or higher qualification, in a relevant discipline (listed below) AND a minimum of five (5) years full-time, or equivalent part-time, management work experience AND among all work experience, a minimum of two (2) years in a relevant professional occupation (listed below).
OR
- Completed Australian doctorate, master’s degree, graduate diploma, graduate certificate, or honours degree, or overseas equivalent, in a relevant discipline (listed below) AND a minimum of five (5) years full-time, or equivalent part-time, management work experience.
OR
- Completed Australian bachelor's degree, or overseas equivalent, or higher qualification AND a minimum of five (5) years full-time, or equivalent part-time, management work experience AND among all work experience, a minimum of three (3) years in a relevant professional occupation (listed below).
Applicants who do not fully meet the work experience requirements may still be considered for an offer. Those applicants may be requested to attend an interview to verify their career seniority and to assess their suitability for the course.
Applicants who do not meet the criteria above should consider applying for C04372 Master of Data Science and Innovation or C06124 Graduate Diploma in Data Science and Innovation or C11274 Graduate Certificate in Data Science and Innovation.
Relevant disciplines:
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
Relevant professional occupations (ANZCO classification):
135 ICT Managers, 2211, 224 Information and Organisation Professionals, 225 Sales, Marketing and Public Relations Professionals, 233 Engineering Professionals, 234 Natural and Physical Science Professionals, 26 ICT Professionals, 313 ICT and Telecommunications Technicians
Supporting documentation to be submitted with the application
- Curriculum Vitae or electronic portflio or professional profile AND Statement of service in one of the following formats:
- A 'Statement of Service' provided by the employer
- A completed 'UTS statement of service’ signed by the employer
- A statutory declaration confirming work experience (for Australian Residents only)
- An official letter from the applicant’s accountant or solicitor on their company letterhead confirming the applicant’s work experience or engagement with the business, duration of operations, and the nature of the business
- A business certificate of registration in original language and English (e.g. provision of ASIC documentation or ABN or similar documentation for Australian Businesses)
Applicants may also submit an optional personal statement, providing additional information to support their application, such as motivation, demonstrated interest, knowledge, skills, experience, understanding / awareness of the course requiremenst and expected commitment to complete the course, and a future plan to pursue an executive career / position in data science.
The English proficiency requirement for international students or local applicants with international qualifications is: IELTS Academic: 6.5 overall with a writing score of 6.0; or TOEFL iBT: 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
| STM91979 EMDSI core subjects | 24cp | |
| CBK92389 EMDSI stream choice | 24cp | |
| 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