8588 modules
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HUMA2026 2027-28
Data Environmentalism
Data is material. It is produced by people, it is made possible by resource extraction, it needs power to survive, it inhabits and resculpts the landscape. The use of data, then, contributes to climate catastrophe, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector.
Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of data and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented data environmentalism. -
COMP1314 2026-27
Data Management
This module aims to:
• Introduce students to the UNIX operating system, to the UNIX command line, and to standard UNIX tools (e.g., vi editor, ed, sed and awk)
• Introduce students to version management systems
• Provide a grounding in the use of database management systems and SQL
• Introduce students to Unix tools for document preparation, software development and system administration -
COMP1314 2025-26
Data Management
This module aims to:
• Introduce students to the UNIX operating system, to the UNIX command line, and to standard UNIX tools (e.g., vi editor, ed, sed and awk)
• Introduce students to version management systems
• Provide a grounding in the use of database management systems and SQL
• Introduce students to Unix tools for document preparation, software development and system administration -
DIGI6005 2025-26
Data Management for Humanities Research
In this module you will develop strategies and skills to integrate data management into humanities data science practices and methods. Over the course of the semester you will learn about good practice guidelines used in humanities research data management and develop skills to interpret and communicate them to a diverse audience of practitioners and researchers. Practical exercises developing data management strategies will enhance your understanding of debates about humanities data science and data driven research in the humanities. By the end of the semester, you will be prepared to situate data science methods in (inter)disciplinary humanities thinking and practically apply them to professional contexts. -
DIGI6005 2026-27
Data Management for Humanities Research
In this module you will develop strategies and skills to integrate data management into humanities data science practices and methods. Over the course of the semester you will learn about good practice guidelines used in humanities research data management and develop skills to interpret and communicate them to a diverse audience of practitioners and researchers. Practical exercises developing data management strategies will enhance your understanding of debates about humanities data science and data driven research in the humanities. By the end of the semester, you will be prepared to situate data science methods in (inter)disciplinary humanities thinking and practically apply them to professional contexts. -
CHEM6167 2026-27
Data Management, Numerical Methods and Analysis
Having learned in semester one how to develop and optimise code to generate new and interesting data, you will now learn how to handle the resulting data and maximise the information retrieved.
This module provides training in advanced numerical methods that will allow in-depth understanding and solving of problems in physical chemistry, computational chemistry, and spectroscopy. It will also provide transferable skills that can be applied to other areas such as data science and quantitative finance. It involves learning to solve problems on a computer by developing code in Python.
The module will also cover data management and procurement, data standards and how to deal with missing or bad data, data reduction, visualisation and error analysis. -
CHEM6167 2025-26
Data Management, Numerical Methods and Analysis
Having learned in semester one how to develop and optimise code to generate new and interesting data, you will now learn how to handle the resulting data and maximise the information retrieved.
This module provides training in advanced numerical methods that will allow in-depth understanding and solving of problems in physical chemistry, computational chemistry, and spectroscopy. It will also provide transferable skills that can be applied to other areas such as data science and quantitative finance. It involves learning to solve problems on a computer by developing code in Python.
The module will also cover data management and procurement, data standards and how to deal with missing or bad data, data reduction, visualisation and error analysis. -
STAT6144 2026-27
Data Mining
New sources of data in a wide range of formats contain valuable information, but extracting this information is often challenging using traditional tools. This module introduces modern techniques for analysing such data and demonstrates how they may be put into action. Methods for handling structured and unstructured data are discussed, including techniques for the analysis of textual data. -
COMP6237 2025-26
Data Mining
The challenge of data mining is to transform raw data into useful information and actionable knowledge. Data mining is the computational process of discovering patterns in data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and data management.
This course will introduce key concepts in data mining, information extraction and information indexing; including specific algorithms and techniques for feature extraction, clustering, outlier detection, topic modelling and prediction of complex unstructured data sets. By taking this course you will be given a broad view of the general issues surrounding unstructured and semi-structured data and the application of algorithms to such data. At a practical level you will have the chance to explore an assortment of data mining techniques which you will apply to problems involving real-world data. -
STAT6120 2025-26
Data Mining
Data analysis is changing. New sources of data in a wide range of formats contain valuable information, but extracting this information is often challenging using traditional tools. This module introduces modern techniques for mining such data and demonstrates how they may be put into action. Methods for handling structured and unstructured data are discussed, including techniques for the analysis of textual data.