Module Catalogue 2024/25

ARA3295 : Fundamentals of Digital Humanities: Computer literacy, data analysis and GIS

ARA3295 : Fundamentals of Digital Humanities: Computer literacy, data analysis and GIS

  • Offered for Year: 2024/25
  • Module Leader(s): Dr Francesco Carrer
  • Lecturer: Dr Louise Rayne
  • Owning School: History, Classics and Archaeology
  • Teaching Location: Newcastle City Campus
  • Capacity limit: 60 student places
Semesters

Your programme is made up of credits, the total differs on programme to programme.

Semester 1 Credit Value: 20
ECTS Credits: 10.0
European Credit Transfer System
Pre-requisite

Modules you must have done previously to study this module

Pre Requisite Comment

N/A

Co-Requisite

Modules you need to take at the same time

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 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

Learning Outcomes

Intended Knowledge Outcomes

- Acquire empirical knowledge of digital, spatial, and non-spatial data in the humanities and social sciences, including data collection and management

- Develop 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

- Learn how to think spatially by identifying patterns in spatial and non-spatial data and using them to make relevant inferences

Intended Skill Outcomes

- Basic use of data portals (Digimap, ADS, ONS etc.)

- Reasonable management of subject-specific datasets

- Transferable computer skills: adequate use of MS Windows functionalities

- Good understanding of Excel: formulas, plots, pivot tables, etc.

- Good understanding of GIS: use of vector and raster geospatial datasets

- Basic competence in exploratory data analysis and interpretation

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion551:0055:00N/A
Scheduled Learning And Teaching ActivitiesLecture51:005:00N/A
Structured Guided LearningLecture materials101:0010:00Recorded lectures, count a contact hours
Guided Independent StudyDirected research and reading351:0035:00Independent reading, based on reading list
Scheduled Learning And Teaching ActivitiesPractical82:0016:00Computer cluster sessions
Guided Independent StudySkills practice371:0037:00N/A
Scheduled Learning And Teaching ActivitiesDrop-in/surgery12:002:00Software surgery
Guided Independent StudyIndependent study401:0040:00N/A
Total200:00
Jointly Taught With
Code Title
ARA8295Fundamentals of Digital Humanities: Computer literacy, data analysis and GIS
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.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Written Examination14401A3024 hour Take home paper on Canvas (approximately 2000 words) aimed at assessing the acquisition of the key concept related to data management, data analysis and mapping (90 minutes expected in 24hours)
Other Assessment
Description Semester When Set Percentage Comment
Poster1A70Poster presenting data visualisation, analysis and/or mapping of a case study, chosen by the student among a series of case studies provided by the lecturers (1000 words)
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Computer assessment1M3 mock exams giving preparation for the online test at the end of the module; one mock exam per teaching block, on Canvas. Time limit 90 mins.
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.

Timetable

Past Exam Papers

General Notes

N/A

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The information contained within the Module Catalogue relates to the 2024 academic year.

In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.

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