Module overview
In this module, you will engage with methods in data science and situate them in critical humanities thinking. We will develop practical skills in using computational methods for working with data, including: dataset and process documentation (e.g. data sheets and model cards), data summarization (e.g. AI), object detection and information extraction (e.g. computer vision), and critical comparisons of approaches to data analysis (e.g. text mining vs corpus linguistics). This module will enable you to situate data science methods in (inter)disciplinary humanities thinking and practically apply them to professional contexts.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Appraise how data science and digital methods are used in industry and professional contexts
- Demonstrate a systematic understanding of methods for doing humanities data science
- Demonstrate critical awareness of current problems and developments in the professional application of humanities data science
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Research and apply appropriate digital methods to a data-driven project, paying due attention to responsibility, integrity, and ethics in research and professional practice
- Analyse data and information using a combination of data science techniques and disciplinary knowledge from the humanities
- Design and deliver a project that uses established techniques of inquiry in humanities data science to create and interpret knowledge
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Act autonomously in planning and implementing tasks at a professional level
- Effectively apply a range of communication techniques to engage diverse and interdisciplinary audiences
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Apply the use of generalist and specialist software for a data-driven project
- Perform practical data science techniques that are informed by environmental and social justice principles
Syllabus
Indicative content includes:
Digital methods and critical humanities research
Problem- and solution-oriented humanities data science
Research ethics and integrity in humanities data science research
Data management and process documentation
Computational methods for data analysis
Extracting information and object identification
Data visualisation
Learning and Teaching
Teaching and learning methods
Students will engage in a combination of self-guided learning, seminar discussions, and hands on practical sessions. This combination of learning strategies will enable creative approaches to understanding how to develop research methods that situate humanistic forms of inquiry within critical data science practices.
| Type | Hours |
|---|---|
| Seminar | 36 |
| Independent Study | 114 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
Textbooks
Fiona Cameron. The Future of Digital Data, Heritage and Curation: in a More-than-Human World. Routledge.
Anna Feigenbaum (Author), Aria Alamalhodaei (Author). The Data Storytelling Workbook. Routledge.
Assessment
Assessment strategy
(1) Project pitch (20%). Students construct a proposal for engaging critically with one or more specific methods in humanities data science in response to a professional brief. (2) Individual project (80%). Students submit a project that applies and critically engages with specific humanities data science methods in response to the professional brief. Students can negotiate the form that this submission will take, including (but not limited to): written report or executive summary; oral presentation; video tutorial. 2,000 words or equivalent.Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Digital project | 80% |
| Project proposal | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Digital project | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Project proposal | 20% |
| Digital project | 80% |