Governance of Data Science - COM00200M

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Module summary

The aim of this module is to introduce students to the governance of data science. Governance of data science will be situated within the broader area of Responsible Research and Innovation and will be linked to trustworthiness and the trust of different stakeholders in the process and outcomes of data science projects.

Professional requirements

n/a

Module will run

Occurrence	Teaching period
A	Semester 1 2026-27

Module aims

The aim of this module is to introduce students to the governance of data science, that is, doing data science projects that adhere to legal, professional, organisational, and moral standards to ensure that the findings are reliable and trustworthy. 

The module will formulate different governance frameworks that can apply to data science projects. Working throughout the data science pipeline, students will develop the ability to identify, describe and analyse how these frameworks apply at each stage of the pipeline. This will be done through students designing and conducting their own data science project using the principles of good governance. The module will also discuss the good communication of data science outcomes for different audiences and using appropriate reporting and visualisation tools. 

Students will explore the governance of data science in real-world contexts. Lectures, practicals and the assessment will draw on real world examples  and case studies from a variety of domains such as public health and social change. Students will develop an awareness of how governance structures and processes are operationalised within these domains.

Module learning outcomes

At the end of the module, students will be able to:

- Articulate and analyse the goals that underpin the governance of data science projects and the governance challenges posed by them;

- Describe and critically analyse key governance frameworks that are applicable to the conduct of data science projects, including legislation and professional codes;

- Identify and interpret different governance frameworks that are applicable to a specific data science project;

- Design and conduct data science projects to be consistent with applicable governance frameworks throughout the data science pipeline;

- Critically evaluate the design and delivery of data science projects against the standards defined in these governance frameworks.

Module content

The module will cover the following topic areas:

• The Data Science pipeline
• Principles of governance from legal, organisational and moral perspectives
• Sources of bias throughout a data science project
• Policy and other governance  frameworks
• The application of good governance throughout the data science pipeline
• Analysis methods such as linear modelling to illustrate governance specifically in the model development process
• Challenges and tension in good governance 
• Case Studies (e.g. Governance of Data Science in Healthcare)

Indicative assessment

Task	% of module mark
Essay/coursework	100.0

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	100.0

Module feedback

Feedback for this module is provided throughout the sessions, and after assessment as per normal University guidelines.

Indicative reading

Jung, D., The Modern Business Data Analyst, Springer, 2024

Martens, D., Data Science Ethics: Concepts, Techniques and Cautionary Tales, OUP, 2022

Spiegelhalter, D., The Art of Statistics, Penguin, 2019