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Master of Data Science

This course is available


Level of Study

Master's Degree


180 credit hours

Next start date

Expected Jul 2023


University of Auckland


Learn how to make decisions using measurable data-driven insights.

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

60 points:

COMPSCI 752 Big Data Management
COMPSCI 760 Datamining and 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 783 Simulation and Monte Carlo Methods
STATS 784 Statistical Data Mining
STATS 787 Topics in Statistical Computing

At least 15 points:

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 Uncertainty in 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 Interactive Cognitive Systems
COMPSCI 767 Intelligent Software Agents
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
HLTHINFO 723 Health Knowledge Management
HLTHINFO 728 Principles of Health Informatics
HLTHINFO 730 Healthcare Decision Support Systems
INFOSYS 700 Digital Innovation
INFOSYS 720 Information Systems Research
INFOSYS 722 Data Mining and Big Data
INFOSYS 737 Adaptive Enterprise Systems
INFOSYS 740 System Dynamics and Complex Modelling
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 752 Research Methods – Modelling
OPSMGT 757 Project Management
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 701 Advanced SAS Programming
STATS 710 Probability Theory
STATS 726 Time Series
STATS 731 Bayesian Inference
STATS 770 Introduction to Medical Statistics
STATS 779 Professional Skills for Statisticians
STATS 780 Statistical Consulting
Other 700-level courses approved by the programme director

45 points:

DATASCI 792, 792a, 792b Dissertation

Entry criteria

Taught 180/240 points

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 4.5.

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.

IELTS (Academic): Overall score of 6.5 and no bands less than 6.0; Internet-based TOEFL (iBT): Overall score of 90 and written score of 21; Paper-based TOEFL: Overall score of 68 and a writing score of 21; Cambridge English: Advanced (CAE) or Cambridge English Proficiency (CPE): Overall score of 176 and no bands below 169; Pearson Test of English (PTE) Academic: Overall score of 58 and no PTE Communicative score below 50; Foundation Certificate in English for Academic Purposes (FCertEAP): Grade of B-; Michigan English Language Assessment Battery (MELAB): 85.

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