Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

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 master’s dissertation work. The students will work with their own datasets during the module leading up to their assessment. Through this module, PGT students will develop the skills that will strength their ability to work with real-world data and to implement effective problem-solving strategies.

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 choose and collate real-world datasets that are more relevant for their research. 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 according to their research interests. 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 - Digital humanities: what they are and why they are important (1 week)
• 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 (2 weeks)
• 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 (3 weeks)
• Examples of data visualisation in 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 (4 weeks)
• 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 (1 week)
• Recap sessions
• Practical exercises
• Q & As
• Software surgeries

Intended Knowledge Outcomes

• Strengthen empirical knowledge of digital, spatial, and non-spatial data in the humanities and social sciences, including data collection and management
• Improve critical thinking skills regarding quantitative data (e.g. hypothesis testing, accuracy vs precisions)
• Understand data sources, data repositories, file management, basic computer skills and computer problem solving
• Attain practical skills in data cleaning, data management, data visualisation and data analysis in Excel
• Understand basic principles of spatial data management and digital mapping
• Developing the ability to describe data management and analysis processes to achieve methodological transparency
• Learn how to think spatially by identifying complex patterns in spatial and non-spatial data and to use them to make relevant inferences

Intended Skill Outcomes

• Competent use of data portals (Digimap, ADS, ONS etc.)
• Effective management of subject-specific datasets
• Transferable computer skills: proficient use of MS Windows functionalities
• Proficient in Excel: formulas, plots, pivot tables, etc.
• Proficient in GIS: Use of vector and raster geospatial datasets
• Basic competence in exploratory data analysis and interpretation

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

The students will choose and find a dataset of interest for their research and will identify a research question that can be addressed through that dataset. The dataset and research question will be discussed with the module leader who will approve a plan of action for each PGT student. Each PGT student will apply theoretical and practical knowledge acquired during the module to produce a poster where patterns in the datasets will be visualised (through maps, plots and/or infographics) and interpreted. The poster will test the ability of the students to use their digital knowledge to investigate a real-world dataset relevant for their research.
The students will also be required to submit a report (2000 words) describing and justifying the methodology they used for their data management, visualisation and analysis. This will save space in the poster, where the students could focus on the research questions, the datasets, and the interpretation.

General Notes

N/A