Science
Master of Data Science
Learning Mode
Full Time
Duration
2 Years
Start date
February, July
Qualification
Master Degree (AQF Level 9)
Fees
AUD 44,160
Location
Melbourne City Campus
About the Course
The Master of Data Science at RMIT University is an advanced and industry-aligned postgraduate program designed to equip you with the expertise to navigate and extract value from complex, large-scale datasets. In today’s data-driven economy, skilled data scientists are critical for innovation and strategic decision-making across virtually every sector. This degree provides a robust foundation in computational methods, advanced statistical analysis, machine learning, and data Visualisation, preparing you to uncover actionable insights.
You will gain hands-on experience with cutting-edge data science tools, programming languages, and big data technologies. The curriculum covers essential areas such as data management, predictive modelling, artificial intelligence concepts, and ethical considerations in data governance. RMIT’s program emphasizes practical application and real-world problem-solving, with opportunities to engage in industry-linked projects. Taught by leading academics and practitioners, you will develop the technical prowess and critical thinking needed to lead data initiatives and drive innovation.
Career prospects
Graduates of the Master of Data Science are in exceptionally high demand across diverse sectors, including technology, finance, healthcare, government, research, and retail. This qualification prepares you for advanced and specialist roles that leverage your analytical and computational expertise to inform strategic decisions, develop new products, and optimize operations. You will be at the forefront of the data revolution, solving complex problems and creating significant value for organisations globally.
Why choose Data Science at RMIT?
Industry-relevant Curriculum
Learn cutting-edge tools and techniques from experts through a curriculum shaped by industry needs.
Practical, Hands-on Application
Gain real-world experience using actual datasets and software through intensive, project-based learning activities.
Interdisciplinary Expertise
Develop cross-disciplinary knowledge in computing, statistics, and domain-specific fields to solve complex data challenges.
Course Overview
The Master of Data Science (MC259) at RMIT University is a 192-credit point (2-year full-time equivalent) postgraduate degree designed to provide advanced expertise in the interdisciplinary field of data science. The program caters to students from a range of academic backgrounds, developing their capabilities in extracting insights from complex data for decision-making.
The course structure typically includes:
- Computational Foundations: Advanced units in programming for data science, algorithms, and big data technologies (e.g., cloud platforms, distributed computing).
- Statistical and Machine Learning Core: In-depth study of advanced statistical modelling, predictive analytics, machine learning algorithms, and deep learning techniques.
- Data Management and Visualisation: Practical skills in managing large, unstructured datasets, database systems, and effectively communicating insights through advanced data Visualisation.
- Applied Projects and Research: Opportunities for industry-linked projects, advanced case studies, or a research project/thesis to apply theoretical knowledge to real-world data challenges.
This comprehensive and applied program ensures graduates are highly skilled and adaptable data professionals.
Course Duration
Length of the course
- 2 years full-time
Mode of Study for international students:
- Full-time enrolment is generally required for international students studying onshore to comply with visa regulations. Online study is available for those studying from their home country.
Course Curriculum
The Master of Data Science curriculum at RMIT is structured to provide a deep theoretical understanding and practical mastery of data science principles and their application across diverse fields.
- Core Data Science and Computational Units These units form the foundation of the master’s degree, covering essential programming, advanced statistical concepts, and computational methods for data. Examples include: Programming for Data Science, Advanced Statistical Modelling, Machine Learning Algorithms, and Big Data Processing.
- Specialised Analytical Techniques and Applications Building on the core, this section delves into more sophisticated methods and real-world applications. Topics might include: Deep Learning and Neural Networks, Natural Language Processing, Data Visualisation and Storytelling, or Data Ethics and Governance.
- Electives and Capstone/Research Project Students can choose elective units to specialised further or broaden their knowledge in areas such as business analytics, cybersecurity, or specific applications of data science in health or finance. The program often culminates in a significant research project or an industry-focused capstone project, allowing for the application of learned skills to a comprehensive data problem.
Admission Requirements
Entry Criteria
- Academic qualifications Applicants must have successfully completed a Bachelor’s degree (or equivalent qualification) in a cognate discipline (e.g., IT, Science, Engineering, Mathematics, Statistics, Business/Commerce with significant quantitative components) from a recognised university, with a minimum Weighted Average Mark (WAM) of 60% or equivalent.
- Alternative Entry: Applicants with a Bachelor’s degree in a non-cognate discipline may be considered if they also have significant relevant professional experience (e.g., 2-3 years in a data-related role) or have completed a Graduate Certificate in a relevant field.
- Prerequisite Knowledge: Demonstrated knowledge in basic programming, statistics, and mathematics is generally expected.
Language Proficiency
- English language requirements (e.g., IELTS, TOEFL) For international applicants, demonstrated proficiency in English is mandatory. This is typically evidenced by:
- IELTS Academic: Overall band score of 6.5, with no band less than 6.0.
- TOEFL iBT: Minimum score of 79, with specific minimum scores for listening (16), reading (13), speaking (18), and writing (21).
- PTE Academic: Overall score of 58, with no communicative skill score less than 50.
Fees and Funding
Tuition Fees:
- AUD 44,160 (Postgraduate – per 1 year full-time for international students).
Scholarships and Bursaries: RMIT University offers a range of scholarships and financial aid opportunities to support eligible students in their Master of Data Science studies. These include:
- RMIT International Scholarships: Various scholarships for high-achieving international students across different disciplines.
- STEM Scholarships: Specific scholarships for students pursuing degrees in Science, Technology, Engineering, and Mathematics.
- Industry-Sponsored Scholarships: Opportunities funded by RMIT’s industry partners for specific IT and data science disciplines.
- Various other bursaries and support schemes are available, designed to assist students with their study costs and enhance their educational experience.
Career Opportunities
Graduates of RMIT’s Master of Data Science program are exceptionally well-positioned for leadership and specialist roles in the global data economy, equipped with advanced analytical and computational skills. The program prepares you for dynamic and impactful careers across virtually all sectors.
Common career paths include: Data Scientist, Lead Data Analyst, Machine Learning Engineer, AI Specialist, Business Intelligence Architect, Data Engineer, Predictive Modeler, Quantitative Analyst, Analytics Consultant, and Research Scientist. These roles span technology giants, financial institutions, healthcare providers, government agencies, research organisations, retail chains, and consulting firms, offering highly rewarding and impactful career trajectories both in Australia and internationally. RMIT graduates are highly sought after for their practical readiness and problem-solving abilities.
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