- Duration
- Full-time: Varies
- Part-time: Varies
- Next start date
- 2025 Semester One – 3 March
- 2025 Semester Two – 21 July
- Available locations
- City
- Points
- 180 or 240
- Programme type
- Postgraduate
- Taught options available
Programme overview
The ability to turn data into information, knowledge and innovative products is a skill in high demand within industry. By completing a strong core of Computer Science and Statistics courses, you will gain a unique combination of skills in Data Science and be able to comprehend, process and manage data effectively to extract value from it. Graduates will be critical, reflective practitioners able to pursue professional goals and further postgraduate study.
We also offer the Master of Data Science (MDataSci) as a 240-point taught masters as a March intake only. This is suitable for students who have a background in either Computer Science or Statistics, but not both. Students who have majored in Data Science, or a combination of computer science and Statistics, should apply for the 180-point taught masters.
Programme structure
180-point taught masters
60 points:
- COMPSCI 752 Big Data Management
- COMPSCI 760 Advanced Topics in Machine Learning
- STATS 763 : Advanced Regression Methodology
- STATS 769 Advanced Data Science Practice
At least 15 points from:
- STATS 705 Topics in Official Statistics
- STATS 730 Statistical Inference
- STATS 767 Foundations of Applied Multivariate Analysis
- STATS 784 Statistical Data Mining
- STATS 786 Time Series Forecasting for Data Science
- STATS 787 Data Visualisation
At least 15 points from:
- COMPSCI 711 Parallel and Distributed Computing
- COMPSCI 720 Advanced Design and Analysis of Algorithms
- COMPSCI 734 Web, Mobile and Enterprise Computing
- COMPSCI 750 Computational Complexity
- COMPSCI 753 Algorithms for Massive Data
Up to 45 points from:
- COMPSCI 705 Advanced Topics in Human Computer Interaction
- COMPSCI 715 Advanced Computer Graphics
- COMPSCI 732 Software Tools and Techniques
- COMPSCI 761 Advanced Topics in Artificial Intelligence
- COMPSCI 765 Modelling Minds
- COMPSCI 767 Intelligent Software Agents
- DIGIHLTH 701 Principles of Digital Health
- DIGIHLTH 702 Health Knowledge Management
- DIGIHLTH 704 Healthcare Decision Support Systems
- ENGSCI 711 Advanced Mathematical Modelling
- ENGSCI 755 Decision Making in Engineering
- ENGSCI 760 Algorithms for Optimisation
- ENGSCI 761 Integer and Multi-objective Optimisation
- ENGSCI 762 Scheduling and Optimisation in Decision Making
- ENGSCI 763 Advanced Simulation and Stochastic Optimisation
- ENGSCI 768 Advanced Operations Research and Analytics
- INFOSYS 700 Digital Innovation
- INFOSYS 720 Information Systems Research
- INFOSYS 722 Data Mining and Big Data
- INFOSYS 757 Project Management and Outsourcing
- MATHS 715 Graph Theory and Combinatorics
- MATHS 761 Dynamical Systems
- MATHS 765 Mathematical Modelling
- MATHS 766 Inverse Problems
- MATHS 769 Stochastic Differential and Difference Equations
- MATHS 770 Advanced Numerical Analysis
- OPSMGT 741 System Dynamics and Complex Modelling
- OPSMGT 752 Research Methods – Modelling
- OPSMGT 760 Advanced Operations Systems
- OPSMGT 766 Fundamentals of Supply Chain Coordination
- SCIENT 701 Accounting and Finance for Scientists
- SCIENT 702 Marketing for Scientific and Technical Personnel
- SCIENT 705 Research Commercialisation
- STATS 710 Probability Theory
- STATS 726 Time Series
- STATS 731 Bayesian Inference
- STATS 732 Foundations of Statistical Inference
- STATS 762 Regression for Data Science
- STATS 770 Introduction to Medical Statistics
- STATS 779 Professional Skills for Statisticians
- STATS 780 Statistical Consulting
- STATS 782 Statistical Computing
- Other 700-level courses approved by the programme director
45 points:
240-point taught masters
The intake for the 240-point taught masters is in March only.
60 points from:
- COMPSCI 717 Fundamentals of Algorithmics
- COMPSCI 751 Advanced Topics in Database Systems
- COMPSCI 762 Foundations of Machine Learning
- DATASCI 709 Data Management
- STATS 707 Computational Introduction to Statistics
- STATS 709 Predictive Modelling
- STATS 762 Regression for Data Science
- STATS 765 Statistical Learning for Data Science
- STATS 782 Statistical Computing
- Other 700-level courses offered at this University approved by the Director
60 points:
- COMPSCI 752 Big Data Management
- COMPSCI 760 Advanced Topics in Machine Learning
- STATS 763 Advanced Regression Methodology
- STATS 769 Advanced Data Science Practice
At least 15 points from:
- STATS 705 Topics in Official Statistics
- STATS 730 Statistical Inference
- STATS 762 Regression for Data Science
- STATS 767 Foundations of Applied Multivariate Analysis
- STATS 784 Statistical Data Mining
- STATS 786 Time Series Forecasting for Data Science
- STATS 787 Data Visualisation
At least 15 points from:
- COMPSCI 711 Parallel and Distributed Computing
- COMPSCI 720 Advanced Design and Analysis of Algorithms
- COMPSCI 734 Web, Mobile and Enterprise Computing
- COMPSCI 750 Computational Complexity
- COMPSCI 753 Algorithms for Massive Data
Up to 45 points from:
- COMPSCI 705 Advanced Topics in Human Computer Interaction
- COMPSCI 715 Advanced Computer Graphics
- COMPSCI 732 Software Tools and Techniques
- COMPSCI 761 Advanced Topics in Artificial Intelligence
- COMPSCI 765 Modelling Minds
- COMPSCI 767 Intelligent Software Agents
- DIGIHLTH 701 Principles of Digital Health
- DIGIHLTH 702 Health Knowledge Management
- DIGIHLTH 704 Healthcare Decision Support Systems
- ENGSCI 711 Advanced Mathematical Modelling
- ENGSCI 755 Decision Making in Engineering
- ENGSCI 760 Algorithms for Optimisation
- ENGSCI 761 Integer and Multi-objective Optimisation
- ENGSCI 762 Scheduling and Optimisation in Decision Making
- ENGSCI 763 Advanced Simulation and Stochastic Optimisation
- ENGSCI 768 Advanced Operations Research and Analytics
- INFOSYS 700 Digital Innovation
- INFOSYS 720 Information Systems Research
- INFOSYS 722 Data Mining and Big Data
- INFOSYS 757 Project Management and Outsourcing
- MATHS 715 Graph Theory and Combinatorics
- MATHS 761 Dynamical Systems
- MATHS 765 Mathematical Modelling
- MATHS 766 Inverse Problems
- MATHS 769 Stochastic Differential and Difference Equations
- MATHS 770 Advanced Numerical Analysis
- OPSMGT 741 System Dynamics and Complex Modelling
- OPSMGT 752 Research Methods - Modelling
- OPSMGT 760 Advanced Operations Systems
- OPSMGT 766 Fundamentals of Supply Chain Coordination
- SCIENT 701 Accounting and Finance for Scientists
- SCIENT 702 Marketing for Scientific and Technical Personnel
- SCIENT 705 Research Commercialisation
- STATS 710 Probability Theory
- STATS 726 Time Series
- STATS 731 Bayesian Inference
- STATS 732 Foundations of Statistical Inference
- STATS 770 Introduction to Medical Statistics
- STATS 779 Professional Skills for Statisticians
- STATS 780 Statistical Consulting
- Any courses listed elsewhere in this schedule or other 700-level courses offered at this University approved by the Director
45 points:
Postgraduate pathway
Download the Science postgraduate pathway (117KB, PDF)
You'll also need to meet other requirements, including time limits and total points limits. See Postgraduate enrolment.
2025 entry requirements
My highest qualification is from:
Programme requirements
Minimum programme requirements
Minimum requirements listed here are the likely grades required and do not guarantee entry. We assess each application individually and applicants may require a higher grade to be offered a place.
-
Study optionTaught 180 pointsGrade requiredGPA Grade Point Average 4.0
Bachelor of Science in Data Science or a Bachelor of Science with a major in Computer Science and a major in Statistics
-
Study optionTaught 240 pointsGrade requiredGPA Grade Point Average 4.0
Bachelor of Science
Further programme requirements
Select your study option:
You must have completed either:
- A Bachelor of Science in Data Science from this University, with a Grade Point Average of 4.0 or higher in 75 points above Stage II.
OR
- A Bachelor of Science with a major in Computer Science and a major in Statistics from this University, with a Grade Point Average of 4.0 or higher in 75 points above Stage II.
You must have completed a Bachelor of Science in a similar field with a GPA of 4.0 in 75 points at Stage lll or above.
Programme requirements
Minimum programme requirements
Minimum requirements listed here are the likely grades required and do not guarantee entry. We assess each application individually and applicants may require a higher grade to be offered a place.
-
Study optionTaught 180 pointsGrade requiredGPE Grade Point Equivalent 4.0
An undergraduate science degree
-
Study optionTaught 240 pointsGrade requiredGPE Grade Point Equivalent 4.0
An undergraduate Science degree
-
QualificationIELTS Academic International English Language Testing SystemScore required6.5
No bands less than 6.0
Further programme requirements
- You must have completed an undergraduate science degree at a recognised university (or similar institution) in a relevant discipline with a Grade Point Equivalent of 4.0.
- Relevant disciplines include data science, or a mixture of computer science and statistics. A minimum amount of study in a relevant discipline is required - this would be at least a major, field of study, or approximately 30 percent of your degree, including a mix of introductory and advanced courses.
How much does a Master of Data Science cost per year?
2025 fees
- Domestic students
- NZ$10,892.40*
- International students
- NZ$52,585 – $52,842*
Fees are set in advance of each calendar year and will be updated on this website. Fees are inclusive of 15% GST, but do not include the Student Services Fee, course books, travel and health insurance, or living costs. Amounts shown are indicative only. In addition to the tuition fees, there is a Student Services Fee of $8.88 per point, estimated at $1,065.60 for full-time study (120 points). Fees will be confirmed upon completion of enrolment into courses.
*Please note: amounts shown are indicative and estimates only.
Find out about financial support information
Scholarships and awards
Find out about the scholarships you may be eligible for.
Student loans and allowances
Are you a New Zealand citizen or resident? You could be eligible for a student loan or allowance.
Cost of living
Get an idea of how much accommodation and general living in Auckland will cost.
Key dates
Please note: We will consider late applications if places are still available. International students should start the application process as early as possible to allow sufficient time to apply for a visa.
Application closing dates
- Semester One 2024
- 8 December 2023
- Semester Two 2024
- 4 July 2024
Where could this programme take you?
With the current demand for continued professional development in this area, this advanced qualification will help you to develop your data science skills to become well-positioned to pursue employment in the data science industry.
Jobs related to this programme
- Business analyst
- Big data solutions architect
- Data mining engineer
- Data scientist
- Digital product designer
- Machine learning engineer
Nishita's graduate story
Read what Nishita has to say about studying Data Science at the University of Auckland.
Read moreStudent career planning service
Once you become a student at the University, you can get help with planning and developing your career from Career Development and Employability Services.
Quick guides to postgraduate Data Science for international students
Experience the University
Meet the programme director
Learn more about the MDataSci programme from Professor Sebastian Link at the School of Computer Science.
Do you need help?
Can’t find the answer in AskAuckland?
Need to speak to someone?
You can phone us directly.
- Auckland
- 923 5025
- Outside Auckland
- 0800 61 62 63
- International
- +64 9 373 7513