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 Datamining and Machine Learning
STATS 762 Regression for Data Science
STATS 769 Advanced Data Science Practice
At least 15 points from:
STATS 705 Topics in Official Statistics
STATS 730 Statistical Inference
STATS 763 Advanced Regression Methodology
STATS 784 Statistical Data Mining
STATS 786 Time Series Forecasting for Data Science
240-point taught masters
The intake for the 240-point taught masters is in March only.
60 points:
COMPSCI 717 Fundamentals of Algorithmics
COMPSCI 751 Advanced Topics in Database Systems
COMPSCI 762 Advanced Machine Learning
STATS 707 Computational Introduction to Statistics
STATS 762 Regression for Data Science
STATS 765 Statistical Learning for Data Science
STATS 782 Statistical Computing
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
Big data solutions architect
Business analyst
Data mining engineer
Data scientist
Digital product designer
Machine learning engineer
Further study options
Doctor of Philosophy
*Price shown is for indicative purposes, please check with institution
4 July 2024
More details
Start date
15 July 2024, 3 March 2025
University of Auckland
City Campus,
Alfred Nathan House, 24 Princes Street,
AUCKLAND CITY,
Auckland Central,
1010, New Zealand
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.
*There may be different IELTS requirements depending on your chosen course.
The University of Auckland is New Zealand’s largest and most highly ranked university, with a global reputation for academic excellence.