Principles of Humanities Data Science

Module overview

In this module, you will be introduced to the principles that underpin humanities data science. We will focus on how to embed a justice-oriented approach in humanities data science, with particular emphasis on climate, feminist, queer, and racial justice. We will explore principles such as minimal computing and lean code, examine histories of hetero-normative and colonial data infrastructures, and consider relationships between volume and complexity in humanistic data. This module will enable you to apply your (inter)disciplinary humanities knowledge to data science and consider how this knowledge can (re)shape data science practices.

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Create a public outcome that responds to and reflects on the principles of justice-led humanities data science
Critically engage with theoretical approaches to combining justice-led and climate-oriented humanities thinking and data science

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Critically evaluate the role of social and environmental justice in data science practices
Demonstrate a systematic understanding of the principles of data science in humanities disciplines
Appraise current problems and developments in the professional application of humanities data science

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Exercise self-direction and orginality in planning and delivering a group project
Engage in critically reflexive practice to support your own professional development
Draw from a range of evidence and knowledge bases to support your arguments for and critical evaluation of justice-led humanities data science principles

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 a critical understanding on the intersections betwene humanistic forms of inquiry and critical data science practices.

Study time
Type	Hours
Seminar	36
Total study time	36

Resources & Reading list

Textbooks

Catherine D'Ignazio and Lauren F. Klein. Data Feminism. MIT.

Jo Gouldi. Dangerous Art of Text Mining. Cambridge University Press.

Fiona R Cameron. Museum Practices and the Posthumanities: Curating for Planetary Habitability. Routledge.

Assessment

Assessment strategy

(1) Group project. (50%) This can be a report, documentary, podcast, website, infographic, code base, or campaign (or other negotiated method of communication) that responds to the principles of justice-led humanities data science. Marks will be awarded to each group, with no difference between individual marks for group members, unless an individual has demonstrably not contributed to the project. (2) Individual portfolio (50%). This portfolio comprises two elements, enabling individual marks to be differentiated.. (a) Reflective report. This is a 1,500 word reflexive report documenting the individual student's role in the group project. (b) Write-up of in-class exercises. Students will write up four in-class exercises, each focusing on a distinct principle of justice-oriented humanities data science taught on the module.

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Essay	50%
Group project	50%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Essay	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.

Breakdown
Method	Percentage contribution
Group project	50%
Essay	50%