Master of Data Science specialising in Data Science
AQF Level 9 • CRICOS Code: 112062K
Advance your career in data science with a comprehensive master’s degree designed to equip you with in-demand technical, analytical, and professional skills. The Master of Data Science at SPI prepares graduates to design, implement, and manage data-driven solutions across complex real-world environments.
Overview
The Master of Data Science (MDS) provides students with comprehensive mastery of all aspects of knowledge and skills related to data science and an opportunity to focus on specialised technical, theoretical or managerial skills through the choice of one specialisation. The MDS places an emphasis on professional practice, effective communication, and project management using technical and non-technical approaches. Students will acquire extensive and deep knowledge to enable them to successfully design, implement and manage data science based solutions in complex real-world applications. Students will become confident in utilising the latest technologies in the data science, significantly enhancing their prospects in career development or entrepreneurship.
Students can nominate a specialisation that allows them to fulfil their own particular interests, their own industry needs, or their own career goals. In particular, students may choose to gain expertise in Artificial Intelligence if they choose not to proceed with the default specialisation.
The MDS is designed for students who have limited experience in, or have been seeking a qualification in, data science. The degree also caters for candidates who already have a sound data science background but wish to update and develop their knowledge and skills to keep pace with the rapid changes in technology and the business opportunities related to the latest advances in data science.
STUDY MODE
On campus
STUDENT
Domestic
International
COURSE
DURATION
2 years
START DATE
09 Mar 2026
06 Jul 2026
12 Oct 2026
View all key dates
FEE
SCHOLARSHIP
Why Study a Master of Data Science at SPI?
Comprehensive Curriculum
Build strong foundations across programming, mathematics, data engineering, analytics, and artificial intelligence.
Industry-Focused Learning
Apply theory through capstone or industry experience projects designed to mirror real-world data science challenges.
Customisable Pathways
Tailor your studies through elective units and optional specialisations aligned with your career goals.
Professional Practice & Ethics
Develop critical research, communication, and ethical decision-making skills essential for data-driven roles.
Australian Computer Society (ACS)
- Industry events and networking opportunities
- Career resources and professional development
- Webinars, technical content, and learning resources
- Access to the ACS member community
Career Opportunities
This program equips you with practical, industry-relevant skills for in-demand technology and digital careers.
Software Developer
Average salary
$75,000 – $110,000
Cybersecurity Analyst
Average salary
$80,000 – $120,000
UX/UI Designer
Average salary
$75,000 – $115,000
Data Analyst
Average salary
$70,000 – $105,000
Systems Analyst
Average salary
$85,000 – $125,000
AI Specialist
Average salary
$95,000 – $150,000
Course Structure
The Master of Data Science is a 2-year AQF Level 9 qualification comprising 96 credit points, including core units, specialisation units, and electives.
Core Units Stream (66 credit points)
This stream provides the core technical and analytical foundations required for advanced data science practice and ensures all students develop the essential skills expected of data science graduates.
Students complete 9 core units (6cp each) across the program, covering programming, mathematical foundations, data management, machine learning, artificial intelligence, research, ethics, and professional practice. The stream culminates in a capstone project or industry experience project, allowing students to apply their skills to real-world data challenges.
Students complete the following core units:
MDS601 – Programming Foundations for Data Science (6cp)
MDS602 – Mathematical Foundations for Data Science (6cp)
MDS603 – Data Science Methodology (6cp)
MDS604 – Database Systems and Infrastructure (6cp)
MDS605 – Machine Learning and Data Mining (6cp)
MDS606 – Data Science Research and Ethics (6cp)
MDS607 – Artificial Intelligence and Innovation (6cp)
MDS608 – Professional Practice (6cp)
and
Select one:
MDS650 – Data Science Capstone Project (18cp)
orMDS651 – Industrial Experience Project (18cp)
Specialisation Core Units Stream (18 credit points)
This stream enables students to build advanced technical capability in key data science infrastructure and analytics areas.
Students complete 3 specialisation core units (6cp each) focusing on data visualisation, big data infrastructure and processing, and cloud computing and application development—equipping graduates to work with large-scale, modern data environments.
Students complete three (3) specialisation core units:
MDS611 – Data Visualization and Tools (6cp)
MDS612 – Big Data Infrastructure and Processing (6cp)
MDS615 – Cloud Computing and Application Development (6cp)
Elective Units Stream (12 credit points)
This stream allows students to customise their learning pathway based on career interests, industry needs, or emerging areas in data science.
Students select 2 elective units (6cp each) from a range of advanced topics, which may include business intelligence, decision analytics, unstructured data, computer vision, natural language processing, autonomous systems, mixed reality, or financial technology.
Elective offerings are subject to availability and may vary by teaching period.
Students complete two (2) elective units from the following options:
MDS609 – Business Intelligence and Analytics (6cp)
MDS610 – Decision Making for Analytics (6cp)
MDS614 – Unstructured Data and Computer Vision (6cp)
MDS617 – Mixed Reality (6cp)
MDS618 – Autonomous Theory and Programming Robots (6cp)
MDS619 – Natural Language Processing and Applications (6cp)
MDS621 – Game Designing (6cp)
MDS622 – Foundations and Applications of Fin-Tech (6cp)
Entry Requirements
Admission to the Master of Data Science is based on meeting SPI’s academic and English language proficiency requirements.
Academic Requirements
Applicants must have completed one of the following:
An AQF Level 7 qualification (such as an approved three-year undergraduate degree) that includes study areas requiring quantitative reasoning and analysis, with a minimum GPA of 4.5; or
Completion of the SPI Graduate Diploma of Data Science or SPI Graduate Certificate in Data Science
(Eligible units may be credited towards the Master of Data Science in accordance with SPI’s Academic Credit and Recognition of Prior Learning Policy); orOther qualifications combined with relevant work or professional experience may be considered on a case-by-case basis.
Relevance of Qualifications
Qualifications presented for admission should have been obtained no more than 5 years prior to the commencement of the program at SPI. Applicants with qualifications older than 5 years are required to provide a statement, demonstrating any of the following:
- the continued relevance and applicability of the student’s prior knowledge to the proposed study at SPI, or
- engagement in continuous learning, or professional development since obtaining the last qualification, or
- any relevant work experience related to the field of study.
This course not only allows those with a related degree to undertake a specialised sequence of units to upskill or refresh their professional IT knowledge, but also serves as an excellent retraining opportunity if a student is a professional aiming to move into a new data science career.
English Language Proficiency
English as a First Language – Applicants must hold citizenship or long-term residency (minimum of 10 years) in an English-speaking country recognised by SPI and have completed secondary or higher education in that country.
English Not as a First Language – Applicants are required to provide a valid English proficiency certificate as proof of English language competency.
Academic Requirements
International applicants must have completed one of the following:
An AQF Level 7 qualification (or equivalent overseas qualification) including study areas requiring quantitative reasoning and analysis, with a minimum GPA of 4.5; or
Completion of the SPI Graduate Diploma of Data Science or SPI Graduate Certificate in Data Science
(Units completed may be credited towards the Master of Data Science subject to SPI policy); orOther qualifications combined with relevant education and/or work experience may be assessed on a case-by-case basis.
Relevance of Qualifications
Qualifications presented for admission should have been obtained no more than 5 years prior to the commencement of the program at SPI. Applicants with qualifications older than 5 years are required to provide a statement, demonstrating any of the following:
- the continued relevance and applicability of the student’s prior knowledge to the proposed study at SPI, or
- engagement in continuous learning, or professional development since obtaining the last qualification, or
- any relevant work experience related to the field of study.
This course not only allows those with a related degree to undertake a specialised sequence of units to upskill or refresh their professional IT knowledge, but also serves as an excellent retraining opportunity if a student is a professional aiming to move into a new data science career.
English Language Proficiency
International applicants must meet one of the following English language requirements:
English Language Test Results
IELTS (Academic): Overall 6.5, with no band less than 6.0
PTE Academic: Overall 58, with no sub-score less than 50
TOEFL iBT: Overall 79, including minimum scores of:
Writing: 21
Reading: 13
Listening: 12
Speaking: 18
TOEFL PBT: Overall 550, including Writing 4.5
Other English language tests recognised as equivalent to IELTS Academic 6.5
English test results must be valid and taken within three (3) years prior to course commencement.
For full admissions requirements please read the Student Admissions and Enrolment Policy and Master of Data Science Admissions Criteria. For SPI Inherent Course Requirements, please refer to the SPI Inherent Course Requirements.
How to Apply
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