Aims

This module aims to introduce students to digital skills commonly used in the humanities and social sciences. By conducting both asynchronous and practical instruction, centred around real data, this module will provide a comprehensive framework for students to think analytically and apply these skills to their dissertation work. This module aims to fill a currently existing gap within the curriculum by providing students with a framework for best practices in GIS, data analysis and data visualization. As evidenced by recent reporting, these skills are not only invaluable in conducting research and solidifying learning outcomes, but also an important aspect of furthering the goal of enhancing employability within the undergraduate student body in skills in which there are national and international shortages.

Outline Of Syllabus

This module will guide students through the processes of data acquisition, cleaning, visualisation, and analysis routinely performed in the human sciences. Students will be provided with real-world datasets and will be able to choose those most relevant in their field. The application of computational tools to these datasets will facilitate the development of a solid protocol for data management and analysis, that the students will replicate and tailor for their own research projects. The module is divided in four sections, each section building on concepts and practical skills covered on previous sections. Key themes of each section are listed below, although additional or alternative themes might be included.


Section 1 – Introduction to digital humanities
Examples of digital humanities research and practices
The theoretical background of digital humanities
Critical and transferable skills
Digital humanities and employability

Section 2 – Data management in the humanities: basic computer skills
Quantifying information in the humanities
Introduction to data types
Finding data: databases, online repositories, web-platforms
Open data and licencing
Data management: acquiring data and creating reasonable file structures
Metadata: what they are, why they are important, and how they are produced

Section 3 - Data analysis and visualisation in Excel
Examples of data visualisation in the humanities
Introduction to descriptive statistics
Filtering and querying
Introduction to statistical inference: correlation, hypothesis testing, confidence interval
Data visualisation: plotting/binning numerical data, plotting categorical data, plotting multiple data

Section 4 – Geographic Information Systems
GIS file formats
Importing data into GIS
Understanding attribute tables
Symbology, queries, labels and visualisation
Joining table to existing vector file
Georeferencing and digitising
Exporting a map

Section 5 – Wrap-up
Recap sessions
Practical exercises
Q & As
Software surgeries

Teaching Rationale And Relationship

Lectures introduce the key themes at the beginning of each section of the module, and the final lecture synthesises the crucial concepts delivered during the module. Recorded material provides background information and practical tutorials. Since this is primarily a skill-based module, the computer cluster sessions and the software surgery enable the students to familiarise themselves with the methods learned from recorded material and lectures and developed through independent reading and skills practice.

Assessment Rationale And Relationship

Digital examination will test the acquisition of basic computational skills and the knowledge of key themes in digital humanities.

The poster will test the ability to use digital tools to produce a clear and informative visualisation of the dataset provided (maps, plots and/or infographics), and to interpret visible patterns and correlations. The digital datasets to be used for the poster will be provided by the module leader.

Formative assessments will be arranged at the end of sections 2, 3 and 4 to test specific skills in data management, data analysis and GIS, in preparation for the digital examination.