BA Politics and Modern History / Course details

Year of entry: 2020

Course unit details:
Beginner's Statistics and Computing in Humanities

Unit code SALC21002
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 2
Offered by School of Arts, Languages and Cultures
Available as a free choice unit? Yes


This course aims to give students a hands-on experience with a broad range of research methods in digital humanities.  The course familiarizes students with computational tools for gathering and exploring textual data, images, and networks, as well as with statistics for humanities.

By the end of the course, the students will have gained fundamental data literacy skills, and they will be taught strategies to continue developing these skills independently.


Unit title Unit code Requirement type Description
Quantitative Methods in Language Sciences LELA20232 Co-Requisite Compulsory

Students taking LELA20231 Quantitative Methods in Language Sciences should not take this module.


The principal aims of the course unit are as follows:

  •  To familiarize students with quantitative assessments of data
  • To help students develop computer skills needed to work with quantitative data
  • To help students understand the rules of describing, visualizing, and interpreting data
  • To explore research questions that can be exploited in research programs within the school
  • To foster critical thinking skills necessary for conducting quantitative research in the future

Learning outcomes


By the end of this course students will be able to:

  • Understand fundamentals of programming
  • Be familiar with basic statistical methods
  • Describe, summarize, and visualize data
  • Conduct basic statistical tests using statistical package R



Week 1. Introduction to digital resources in the humanities.

  Introduction to R. Building a database.

Week 2. Data summaries, descriptive statistics, logical indexing

Week 3. Visualising data I

Week 4. Visualising data II

Week 5. Basic statistical tests

Week 6. Analysing text I: tokenisation, lemmatisation, pos-tags, syntactic tags.

Week 7. Giving an oral presentation (lecture). Building a concordancer.

Week 8. Analysing text II: informational retreival, topic modelling

Week 9. Geospatial data

Week 10. Image data

Week 11.Revision

Teaching and learning methods


  • Weekly 2h seminars
  • E-learning: The Blackboard environment will provide video tutorials on using the programming languages (R, Python), quizzes, additional exercises and a discussion forum with peer-to-peer support in coding
  • Lectures (2h in week 1 and 1h in Week 7)
  • Surgeries (3h)

Knowledge and understanding


By the end of this course students will:

  • Know how to compile data sets suitable for computational and statistical analysis for various media (text, image, etc.)
  • Know how to efficiently search large digital collections of data
  • Know different descriptive statistics used to summarize data
  • Know different kinds of plots for data visualization
  • Understand the assumptions behind basic statistical tests
  • Conduct basic statistical tests using R

Intellectual skills


By the end of this course students will be able to:

  • Assess validity and soundness of conclusions drawn from basic statistical tests
  • Know the difference between speculation and empirically observed and statistically verified tendencies
  • Understand (current) challenges in digital humanities, including incompleteness and size of data sets
  • Apply statistical testing and computing within the context of their discipline

Practical skills


By the end of this course students:

  • Will be able to use computer programs to visualize and summarize data and conduct basic statistical tests.
  • Will be able to write simple computer code
  • Will be able to communicate their research output in written and verbal form

Transferable skills and personal qualities


By the end of this course students will be able to:

  • Use a variety of quantitative techniques to explore quantitative data
  • Become comfortable working with quantitative data
  • Develop presentation skills
  • Develop time management skills by working to deadline

Employability skills

By the end of the semester, students are expected to develop data literacy and coding skills. These skills will be a major asset to them in the job market, as employers increasingly look for candidates who combine domain-specific knowledge (e.g. history, modern languages) with general data analysis skills. The students will also develop presentation skills, which will help them perform in job interviews, and in professional presentations.

Assessment methods


Assessment task

Formative or summative


Weighting within unit

(if summative)



2 hours


Mini-conference presentation (workload equivalent to a 2,500 word assignment - presentation slides to be submitted before mini-conference)


10 mins per student


Two BB quizzes


30 minutes (each)



Feedback methods


Feedback method

Formative or summative

The instructors will provide individual help and feedback on the in-class exercises


Blackboard quizzes will allow students to check their understanding of the material


Feedback from the instructor and from other students will be provided on the oral presentations

formative and summative

Additional one-to-one feedback will be provided as required during consultation hour or by appointment



Recommended reading


Arnold, T. & Tilton, L. (2015). Humanities Data in R. Springer 


Study hours

Scheduled activity hours
Assessment practical exam 6
Lectures 3
Practical classes & workshops 3
Seminars 22
Independent study hours
Independent study 166

Teaching staff

Staff member Role
Lauren Fonteyn Unit coordinator
Patrycja Strycharczuk Unit coordinator

Additional notes


  • 22h of seminars
  • 3h of lectures
  • 3h of surgeries
  • 6h of presentations
  • 2 x 1h mandatory attendance at research seminar (DH@Manchester, LEL Research Seminar, …)

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