Data Science in Society
| Module title | Data Science in Society |
|---|---|
| Module code | SPA3009 |
| Academic year | 2025/6 |
| Credits | 15 |
| Module staff | Dr Niccolo Tempini (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 30 |
|---|
Module description
This module will focus on the societal context for data science, machine learning and artificial intelligence. An increasing number of social, governmental and commercial processes now take place in online or digital environments, making it important to consider the ways in which data is used to make decisions and how the application of computational methods to engineer social processes can be managed in ways that are ethical, transparent and socially acceptable. This module will teach you the core knowledge around data ethics, privacy, fairness and data governance. You will be encouraged to form your own opinions on how digital tools can best be developed to deliver benefits and avoid harm. Seminar discussions and ethical case studies will be used to highlight and explore different social issues around data science.
Suitable for non-specialists and interdisciplinary pathways
Module aims - intentions of the module
This module aims to:
- Explore how data science and analytics can utilised in ways that are responsive to broader social concerns in the UK and internationally;
- Discuss data ethics and societal issues around data collection, processing, dissemination and use in decision-making;
- Enhance knowledge of data protection policies at local, national and international level;
- Provide skills in engaging data providers, users and customers to ensure compliance with regulations and legal systems;
- Ensure awareness of social context of data science with attention to potential ethical concerns including privacy, fairness and bias;
- Develop skills in assessing data science services (and particularly Big Data) for the general public as well as specific stakeholders (government, local authorities, competitors in industry, lobby groups).
The module will draw on recent scholarship in data studies and case studies based on a variety of contexts. The module provides training in ethical and societal implications of data collection, processing and management strategies.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Show critical Understanding of key terms and concepts in data ethics, data governance and information management and use them in an articulate and comprehensive way, orally and in writing
- 2. Understand, contextualise and critically evaluate key challenges in the management of data collection, processing, storage and dissemination within current legal frameworks
- 3. Critically reflect on, evaluate and comprehensively contrast and compare key ethical issues relevant to data science and how ethical concerns can be managed
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Critically understand, apply and criticise theoretical arguments, frameworks and concepts from data studies to the use of data science in an organisational context
- 5. Demonstrate data science problem thinking that tackles diverse socio-technical issues at once, imagining and evaluating different courses of action
- 6. Develop knowledge and understanding of professional responsibility and ethics around data science
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. Analyse and draw conclusions from unstructured social scenarios and real-world cases
- 8. Demonstrate skills of critical and reflexive thinking, and effective independent study and research
- 9. Plan, execute and write-up effective independent study and research
- 10. Collaborate with peers in a team and manage a team-based project
Syllabus plan
Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:
- The role of data science in society;
- Basic concepts of data ethics and data governance;
- Applying current concepts in data ethics and data governance to real examples;
- Understanding the legal frameworks around use and analysis of data in social and business contexts;
- Intellectual Property, legal regimes and regulatory structures around the ownership and maintenance of databases;
- Data dissemination, curation and Open Data, the limits of automation, and the challenges of making data accessible and re-usable;
- Data protection and legal frameworks for data collection, storage and analysis.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 22 | 11 x 2 hours per week comprising of lectures and seminars |
| Guided Independent Study | 128 | Reading, seminar preparation, coursework |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Group topic plan | Max 1500 words | 1-10 | Written feedback |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Essay | 100 | 2000 words | 1-10 | Written feedback |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Essay (2000 words) | Essay (2000 words) | 1-10 | Referral/Deferral period |
Indicative learning resources - Basic reading
- Schutt, R. and O’Neill, C., Doing Data Science: Straight Talk from the Frontline.
- Boyd, D. and Crawford, K., Six provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society.
- Kitchin, R., The Data Revolution.
- Leonelli, S., Data-Centric Biology: A Philosophical Study.
- Science International (2015). Big Data in an Open Data World.
- Hey, Tony, et al. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Publishing.
- Hine, Christine. 2006. ‘‘Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work.’’ Social Studies of Science 36 (2): 269-98.
- Dove, Edward S., Yann Joly, Anne-Marie Tassé, Paul Burton, Rex Chisholm, Isabel Fortier, Pat Goodwin, et al. 2015. “Genomic Cloud Computing: Legal and Ethical Points to Consider.” European Journal of Human Genetics 23 (10): 1271–78. doi:10.1038/ejhg.2014.196.
- Dove, Edward S., David Townend, Eric M. Meslin, Martin Bobrow, Katherine Littler, Dianne Nicol, Jantina de Vries, et al. 2016. “Ethics Review for International Data-Intensive Research.” Science 351 (6280): 1399–1400. doi:10.1126/science.aad5269.
- Burton, Paul R., Madeleine J. Murtagh, Andy Boyd, James B. Williams, Edward S. Dove, Susan E. Wallace, Anne-Marie Tassé, et al. 2015. “Data Safe Havens in Health Research and Healthcare.” Bioinformatics 31 (20): 3241–48. doi:10.1093/bioinformatics/btv279.
- Boulton, Geoffrey, Brian Campbell, Brian Collins, Peter Elias, Wendy Hall, Graeme Laurie, Onora O’Neill, et al. 2012. “Science as an Open Enterprise.” 02/12. London: The Royal Society Science Policy Centre.
Key words search
Data Science; Data Ethics; Data Governance; Data Protection
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | Cannot have taken SPA2009 |
| Module co-requisites | None |
| NQF level (module) | 6 |
| Available as distance learning? | No |
| Origin date | 12/04/2019 |
| Last revision date | 27/02/2024 |
