Skip to main content
Panoramic collage of university campus architecture

Master of Data Science

University of Auckland, New Zealand
Save

Course 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.

General Eligibility

Study option

Taught 180/240 points

Grade required

GPA 4

What You'll Study

1 Programme structure

Core courses (60 points)

  • Big Data Management

  • Data Mining and Machine Learning

  • Regression for Data Science

  • Advanced Data Science Practice

Elective courses (45 points)

  • Topics in Official Statistics

  • Statistical Inference

  • Advanced Regression Methodology

  • Statistical Data Mining

  • Time Series Forecasting for Data Science

  • Data Visualisation

  • Parallel and Distributed Computing

  • Advanced Design and Analysis of Algorithms

  • Web, Mobile and Enterprise Computing

  • Computational Complexity

  • Algorithms for Massive Data

  • Advanced Topics in Human Computer Interaction

  • Advanced Computer Graphics

  • Software Tools and Techniques

  • Advanced Topics in Artificial Intelligence

  • Interactive Cognitive Systems

  • Intelligent Software Agents

  • Advanced Mathematical Modelling

  • Decision Making in Engineering

  • Algorithms for Optimisation

  • Integer and Multi-objective Optimisation

  • Scheduling and Optimisation in Decision Making

  • Advanced Simulation and Stochastic Optimisation

  • Advanced Operations Research and Analytics

  • Health Knowledge Management

  • Principles of Health Informatics

  • Healthcare Decision Support Systems

  • Digital Innovation

  • Information Systems Research

  • Data Mining and Big Data

  • Adaptive Enterprise Systems

  • System Dynamics and Complex Modelling

  • Graph Theory and Combinatorics

  • Dynamical Systems

  • Mathematical Modelling

  • Inverse Problems

  • Stochastic Differential and Difference Equations

  • Advanced Numerical Analysis

  • Research Methods - Modelling

  • Project Management

  • Advanced Operations Systems

  • Fundamentals of Supply Chain Coordination

  • Accounting and Finance for Scientists

  • Marketing for Scientific and Technical Personnel

  • Research Commercialisation

  • Advanced SAS Programming

  • Probability Theory

  • Time Series

  • Bayesian Inference

  • Introduction to Medical Statistics

  • Professional Skills for Statisticians

  • Statistical Consulting

Dissertation (45 points)

  • Dissertation

Language Requirement

  • IELTS6.5
  • TOEFL93
  • PTE65
  • Duolingo89

The IELTS score for international applicants is 6.5 (with no less than 6 in each component).

Ready to get started?

For any enquiries or further details, please don't hesitate to contact us. Let's take the next step in your academic journey together.

Contact Us
Call
WhatsApp
Email