HIS32730 The Digital Humanities

Academic Year 2024/2025

The digital humanities are powering huge transformations in the way history is explored. Vast mountains of data can now be mined in ways previously unimaginable. This course offers a hands-on introduction to a range of core techniques employed in the field. The class will be taught through short presentations by the module coordinator, but more especially practicals which will lead you step by step through areas such as: data harvesting (extracting information from the catalogue of the Imperial War Museum), relational databases (slave trade), visualization techniques, deep learning, text analysis/mining, and using Python (a simple but incredibly powerful code). Through these eye-opening introductory sessions, together with the final individually-tailored research project you will work on, we hope to build your confidence in the field. More broadly, the course will help improve your awareness of what is possible in your future research or indeed to real-world scenarios, helping to draw your attention both to the opportunities as well as the limitations of technology-driven research approaches.

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Curricular information is subject to change

Learning Outcomes:

On completion of this course, you will have
• Gained an awareness of some of the ways in which technology is employed in humanities research
• Have been introduced to, and developed confidence in using, some of these techniques yourself
• Developed your ability to take general concepts and techniques and find applications within your own research areas
• Honed problem-solving skills in using and applying technology
• Developed an awareness of how major digital projects are constructed, and be able to identify inherent opportunities and limitations in the methodologies employed

Indicative Module Content:

1. Introductions
2. Data Harvesting and Web Scraping
3. Relational Database Principles & Design (LibreOffice)
4. Relational Database Queries (LibreOffice)
5. Data Visualisation including GIS (Microsoft Excel & Tableau)
6. READING WEEK
7. Deep Learning, Neural Networks & Image Recognition
8. Text Analysis and Mining (Sketch Engine)
9. PYTHON: An Introduction
10. Data Management Principles
11. Presentations on Projects

Student Effort Hours: 
Student Effort Type Hours
Seminar (or Webinar)

10

Project Supervision

2

Specified Learning Activities

95

Autonomous Student Learning

95

Online Learning

20

Total

222

Approaches to Teaching and Learning:
You will learn primarily through weekly practicals (Active task-based learning; case-based learning), but also
by working on a digital research project of your own supported by one to one meetings with the module coordinator.

The practicals typically involve a short video introduction and detailed step by step instructions. These will be undertaken at your convenience each week. A coffee ZOOM session will be held every Thursday morning to address issues you might be facing, and to give us the opportuntiy to reflect on some of the uses to which digital research techniques can (and cannot) be put.


 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade

Not yet recorded.


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.