Postgraduate

DATA401
Introduction to Data Science
Description
This course covers the development of statistical concepts and their application to complex systems.
Occurrences
Semester One 2026
Semester One 2026 (Distance)
Semester Two 2026
Semester Two 2026 (Distance)
Special non-calendar-based Two 2026 (UC Online)
Special non-calendar-based Four 2026 (UC Online)
Points
15 points
Prerequisites
Subject to approval of the Head of School.
DATA415
Computational Social Choice
Description
This course provides a thorough introduction to both classical and computational social choice. Social choice theory is the study of mechanisms for collective decision making, such as voting rules or protocols for fair division. Computational social choice addresses problems at the interface of social choice theory with computer science, it uses concepts from social choice theory in the presence of big datasets. This course will introduce some of the fundamental concepts in social choice theory and how they are used in today's data science. The topics covered include material in voting theory, preference aggregation, judgment aggregation, and fair division.
Occurrences
Semester Two 2026
Semester Two 2026 (Distance)
Special non-calendar-based One 2026 (UC Online)
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA416
Contemporary Issues in Data Science
Description
This course focuses on the technical challenges in data science that societal and regulatory actions pose. It aims to introduce students the often very different and sometimes even conflicting perspectives from which policymakers and the technical community approaches these problems. We will review and discuss different examples from different areas of data science such as the extent to which machine learning and deep learning techniques conform with GDPR regulations on transparency, explainability, and accountability; impossibility theorems showing off the limits of data science methods; the mathematical foundations and data science techniques for mechanism design in order to manipulate beliefs (represented as transitive, anti-symmetric, and complete binary relations); and provide students as potential future product developers with the necessary knowledge to engage in responsible product development practices that are informed by regulatory requirements and expectations. This course develops students' understanding of the role of data science in decision making and the impact of data science in the design of AI systems. The course reflects the main issues of controversy identified in international policy debates.
Occurrences
Semester One 2026
Semester One 2026 (Distance)
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA420
Scalable Data Science
Description
This course will introduce students to core topics in scalable data science based on distributed-computing techniques. This is a very practical course, with students learning by experimenting on a computer cluster.
Occurrences
Semester One 2026
Semester One 2026 (Distance)
Semester Two 2026
Semester Two 2026 (Distance)
Special non-calendar-based Two 2026 (UC Online)
Special non-calendar-based Four 2026 (UC Online)
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA422
Data Wrangling for Data Science
Description
This course develop students skills in data cleaning and processing, data integration techniques and implementing data wrangling workflows for a real world datasets.
Occurrences
Semester Two 2026
Semester Two 2026 (Distance)
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA423
Data Science in Industry
Description
In this course we will address core topics in the application of data science in industry.
Occurrences
Semester One 2026
Semester One 2026 (Distance)
Semester Two 2026
Semester Two 2026 (Distance)
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA424
Information Is Beautiful
Description
This course will introduce students to the truthful art of visualizing data. The students will use an iterative design process to create visualizations that are truthful, functional, beautiful, insightful and enlightening. The lectures will consist of presentations, critiques, in-class exercises and discussions. This course will enable students to select appropriate visualization methods for their data and solve practical data science communication problems. They will consider the context and the indented reader to focus the story their data will tell. The students will learn to use the Tableau software, which will be made available for their own computers within the framework of this course. The course will provide a supportive environment in which students can experiment with the aesthetics of data visualization. Students will need to be familiar with basic data manipulation principles and the process of data gathering and cleaning.
Occurrences
Semester Two 2026
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA425
Foundations of Deep Learning
Description
The aim of this course is to introduce students to foundational concepts of deep neural networks. The focus of this course is on both fundamental and applied methods in deep neural networks. A range of topics from convolutional and recurrent type networks to neural-network generative models and attention mechanisms will be introduced.
Occurrences
Semester One 2026
Points
15 points
Prerequisites
Subject to HoS approval
Restrictions
DATA473
DATA428
Data Science Project
Description
This course provides students with an opportunity to develop data science skills to extend and strengthen their understanding of an area of data science.
Occurrences
Semester One 2026
Semester Two 2026
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA429
Data Science Independent Study
Description
This course provides students with an opportunity to develop data science skills in a specific area of data science. The intent of the course is to provide students with an opportunity to work on a data science industry topic with an academic supervisor.
Occurrences
Summer Nov 2025
Semester One 2026
Semester Two 2026
Points
15 points
Prerequisites
Subject to approval of the Head of Department of Mathematics and Statistics.
DATA473
Special Topic
Occurrences
Semester Two 2026
Points
15 points
Prerequisites
Subject to the approval of the Head of School.
DATA480
Research Project
Description
Project
Occurrences
Whole Year 2026
Points
30 points
Prerequisites
Subject to the approval of the Programme Director

Not Offered Courses in 2026

Postgraduate

DATA417
The Trustworthy Data Scientist
Description
This course will stimulate students to think about the ethical facets of their data scientific projects and provide them with conceptual and practical tools to assess said project. The ethics and security of data collection, storage, manipulation, analysis and communication is of paramount importance in our information based society. This course faces these topics from the point of view of data scientists-rather than consumers or data subjects-enabling the student to become trustworthy professionals. The students will learn to identify risk and opportunities related to fairness, agency, interpretability, and security. Maori Data Sovereignty, Te Mana Raraunga, and its relevance for data scientist in New Zealand will be introduced. The course will follow a flipped class-room flow. Fundamental concepts will be first introduce via guided discussions and hands-on-data exercises during the laboratories. In the lectures, the understanding of concepts and tools introduced in the laboratories is made rigorous and generalised.
Occurrences
Not offered 2026, offered in 2020 , 2021 , 2022 , 2023 , 2024
For further information see DATA417 course details
Points
15 points
DATA419
Online Communities and Social Networks
Description
This course introduces to the analyses of online communities and social networks. Students will learn to formulate scientific questions about the online dynamics and use open software to collect, organize, model, and communicate data from common social networks.
Occurrences
Not offered 2026, offered in 2020 , 2021 , 2022 , 2023
For further information see DATA419 course details
Points
15 points
DATA430
Medical Data Informatics
Description
This course explores statistical models, algorithms, and programming platforms for medical data including imaging, clinical and research text reports, lab results, and patient records.
Occurrences
Not offered 2026
For further information see DATA430 course details
Points
15 points
DATA472
Special Topic
Occurrences
Not offered 2026
For further information see DATA472 course details
Points
15 points
DATA474
Special Topic: Mathematical Data Science
Occurrences
Not offered 2026, offered in 2024 , 2025
For further information see DATA474 course details
Points
15 points