Master of Mathematical Sciences specialising in Artificial Intelligence

2 Years On Campus Masters Program

University of Adelaide

Program Overview

The Master of Data Science specialising in Mathematics at the University of Adelaide develops advanced skills across the data science pipeline, combining mathematics, statistics, computing and data analysis to prepare students for data-driven careers. It suits students who want to build strong mathematical foundations while applying them to areas such as machine learning, artificial intelligence, forecasting and large-scale data analysis, with a final-year industry-focused capstone experience.

Curriculum Structure

Year 1: Students establish the mathematical and statistical foundations needed for advanced data science through Mathematics for Data Analytics A (MATH5109), covering linear algebra, matrices, calculus and their applications to data science, followed by Mathematics for Data Analytics B (MATH5110), which develops optimisation, multivariable calculus, probability and entropy, including the use of Python to verify solutions. Statistical Foundations for Data Science and Artificial Intelligence (STAT5020) then builds skills in exploratory data analysis, statistical inference, data transformation and regression.

Year 2: Students deepen their specialist data science capabilities through discipline options such as Time Series Analysis and Forecasting (STAT6002), Neural Networks and Deep Learning (ARTIX300), Generative Artificial Intelligence (ARTI5001) and Large Language Models and Applications (COMP6012). The program culminates in ICT Master Capstone Project 1 (COMP6024) and ICT Master Capstone Project 2 (COMP6000), giving students the opportunity to integrate their mathematical, statistical and computational skills in a substantial project with real-world relevance.

Focus areas: Mathematical foundations for data analytics, linear algebra, calculus, probability, statistical modelling, data analysis, optimisation, machine learning, artificial intelligence, forecasting and large-scale data science.

Learning outcomes: Graduates develop the ability to apply advanced mathematical and statistical methods to data problems, analyse and interpret complex datasets, use computational approaches to solve data science problems, evaluate data-driven solutions and communicate analytical results clearly.

Professional alignment (accreditation): The program provides a multidisciplinary foundation spanning mathematics, statistics, computer science and data science, aligning with the technical skills required across modern data-driven industries. The university identifies mathematics and data science graduates as being sought after across marketing, healthcare, government and finance.

Reputation (employability rankings): Adelaide University is ranked 51–100 globally for Data Science and Artificial Intelligence in the QS World University Rankings by Subject 2026, demonstrating the university's strong international standing in this field.

Experiential Learning (Research, Projects, Internships etc.)

The Master of Data Science specialising in Mathematics at the University of Adelaide provides a strongly applied learning experience, combining mathematical modelling, statistics, programming and data analysis. Students build practical capability through problem-solving tasks, mathematical computing, data visualisation and programming, with Python used to implement mathematical methods and R used in statistical work. The program also includes a substantial work-integrated learning capstone project, allowing students to apply their data science skills to real-world problems in collaboration with industry:

Python programming: In Mathematics for Data Analytics B, students use Python to verify mathematical solutions and perform more complex computations involving linear algebra, calculus and probability.
R statistical software: Statistical Foundations for Data Science and Artificial Intelligence specifically lists RStudio as a course resource, supporting statistical analysis, data transformation, visualisation and regression modelling.
Data visualisation tools: Students learn to create visualisations using code-based tools such as ggplot2 in R, while developing portfolios and communicating insights through visual storytelling.
Mathematical computing: The mathematics component covers linear algebra, multivariable calculus and probability, with practical problem-solving tasks that require students to apply these concepts to data analytics.
Industry capstone projects: The program includes ICT Master Capstone Project 1 and 2, together worth 12 units, providing structured work-integrated learning and real-world project experience.
Industry collaboration: Adelaide University states that mathematics and data science students can undertake real-world projects with university partners, providing exposure to practical data challenges and professional practice.
Purpose-built computer laboratories: Students have access to purpose-built computer laboratories designed for mathematics and data science, where they can develop coding and digital skills.
Practical and group-based assessment: The program's learning experience can include practicals, group projects, research projects, reports, presentations and project documentation, giving students several ways to demonstrate applied skills.
Maths Clinic: Students may have access to the university's specialised Maths Clinic, designed to strengthen communication, critical thinking and problem-solving through applied mathematical work.
Libraries and digital learning: Adelaide University's campuses provide libraries, computer suites and online resources, while its learning environment is supported by a 24/7 online learning platform with personalised study information and resources.
Research environment: The program sits within the School of Mathematical Sciences, which supports postgraduate and research pathways in mathematics, data science, statistics and artificial intelligence.
Internships and placements: Adelaide University provides access to work placement and internship opportunities, alongside networking events, guest speakers and overseas study opportunities.

Progression & Future Opportunities

The Master of Data Science – Specialisation in Mathematics at the University of Adelaide prepares graduates for advanced data-driven careers across sectors such as finance, healthcare, technology, marketing and cybersecurity. The mathematics-focused pathway is particularly valuable for students who want to combine statistical and mathematical modelling with modern data science, while the program also develops skills suited to research and strategic data science roles.

Typical career opportunities include:

Data Scientist
Data Engineer
Machine Learning Engineer
Research Scientist
Statistician
Data Modeller

The University of Adelaide supports your transition from study to employment through:

Career Services: Students can access career planning, employment preparation, networking and industry events, job opportunities, workshops and career advice. The university's CareerHub also provides graduate, work-experience and part-time opportunities, employer information and job alerts.
Graduate career support: After graduation, students can receive personalised career advice and support for up to 12 months, followed by lifetime access to digital career resources.
Industry experience: The program includes a hands-on industry project in which students work with an industry client, apply their technical expertise to a real-world problem and develop professional networks.
Industry connections: Adelaide University states that it is building on 7,000 partnerships with industry and collaborators, providing a broad environment for industry engagement across technology, computing and AI.
International mentoring: International students can participate in the International Alumni Mentoring Program, gaining insights into industry trends, employer expectations and career development from mentors with similar academic or cultural backgrounds.
Professional accreditation: The Master of Data Science is provisionally accredited at the Professional level by the Australian Computer Society (ACS). Graduates are eligible for ACS membership, with recognition internationally through the Seoul Accord, adding valuable professional recognition to the qualification.
Graduate outcomes: Adelaide University identifies data science as a rapidly expanding field and notes that data science is among Australia's top five most in-demand and highest-paying sectors, according to the Analytics Institute of Australia.
Salary figures: The university's official program information does not publish a specific graduate salary figure for this Master of Data Science, so a university-source salary figure should not be assumed or substituted with third-party estimates.

Further Academic Progression: After completing the Master's degree, students interested in advanced research can progress into Master of Research, Master of Philosophy or Doctor of Philosophy (PhD) pathways within the School of Mathematical Sciences. The university specifically lists these research degrees alongside Data Science postgraduate study, making the Master's a strong foundation for students who want to move into specialised research, academia or R&D careers.