Science
Master of Data Science
Learning Mode
Full Time
Duration
2 Years
Start date
March, July
Qualification
Master Degree (AQF Level 9)
Fees
AUD 43,000
Location
Burwood (Melbourne), Waurn Ponds (Geelong)
About the Course
The Master of Data Science at Deakin University is an advanced postgraduate program meticulously designed to transform you into a highly capable data professional ready to tackle the complexities of big data. In an era where data is pivotal, this degree equips you with sophisticated knowledge and practical skills in data analysis, interpretation, and strategic application across diverse industries. You will master cutting-edge statistical methodologies, machine learning algorithms, and computational tools essential for extracting meaningful insights from vast datasets.
The program offers a comprehensive curriculum covering data management, predictive modelling, artificial intelligence concepts, and data Visualisation, all taught with an emphasis on real-world problem-solving. You will gain hands-on experience with industry-standard software and techniques, preparing you to lead data-driven initiatives. Deakin’s strong industry connections and a focus on applied research ensure you graduate with highly sought-after capabilities, ready to innovate and make significant contributions to the data science landscape.
Career prospects
Graduates of the Master of Data Science are in exceptional demand globally, with roles spanning technology, finance, healthcare, government, research, and consulting. This qualification prepares you for advanced and specialist positions that leverage your analytical and computational expertise to drive innovation and inform critical decision-making. You will be at the forefront of the data revolution, solving complex problems and creating strategic value for organisations.
Why choose Data Science at Deakin?
Industry-aligned curriculum
Developed with industry input, the curriculum teaches cutting-edge tools and skills that meet evolving data science demands.
Practical, hands-on experience
Gain experience using real datasets, software, and projects in data visualisation, analysis, and machine learning applications.
Strong research opportunities
Collaborate with expert faculty on advanced data science research, sharpening inquiry skills and preparing for doctoral pathways.
Course Overview
The Master of Data Science (S740) at Deakin University is a 12-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:
- Foundational and Advanced Computing: Units covering programming for data science, algorithms, and big data technologies.
- Statistical and Machine Learning Core: In-depth study of advanced statistical modelling, predictive analytics, and machine learning algorithms.
- Data Management and Visualisation: Practical skills in managing large datasets, database systems, and effectively communicating insights through advanced data Visualisation techniques.
- Research Project or Capstone Experience: A significant independent research project or an industry-focused capstone unit, integrating theoretical knowledge with practical application.
This comprehensive and applied approach 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 to comply with visa regulations.
Course Curriculum
The Master of Data Science curriculum is designed 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 for Data Science, and Big Data Management.
- Specialised Analytical Techniques and Applications Building on the core, this section dives into more sophisticated methods and real-world applications. Topics might include: Deep Learning, Natural Language Processing, Data Ethics and Governance, or electives in areas like Business Analytics or Cybersecurity.
- Research Project or Industry Capstone The program culminates in a substantial independent research project (minor thesis) or an industry-focused capstone unit. This allows students to apply their accumulated knowledge to a complex data problem, develop advanced research skills, or gain practical industry experience.
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 with significant quantitative components) with a minimum Weighted Average Mark (WAM) of 60% or equivalent.
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- 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 reading (13), listening (12), 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 43,000 (Postgraduate – per 1 year full-time for international students)
Scholarships and Bursaries: Deakin University offers a range of scholarships and financial aid opportunities to support eligible students in their Master of Data Science studies. These include:
- Deakin Vice-Chancellor’s Academic Excellence Scholarship: For high-achieving international students.
- Faculty of Science, Engineering and Built Environment Scholarships: Specific scholarships offered by the faculty for postgraduate students demonstrating academic merit or research potential.
- Deakin HDR Scholarships: For students pursuing research components or pathways to PhD.
- Various other bursaries and support schemes are available, designed to assist students with their study costs and enhance their educational experience.
Career Opportunities
Potential job roles and industries Graduates of Deakin’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, Senior 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.
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