BA Latin and English Literature / Course details

Year of entry: 2024

Course unit details:
Data Literacy in a Digital World

Course unit fact file
Unit code SALC20081
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 1
Available as a free choice unit? Yes


The rate at which data are created is mind-blowing. Ninety percentage of all data produced by mankind was created over the last 20 years. All of us encounter the results of data analysis regularly in our everyday lives. Virtually all jobs in the modern world require good data fluency – being able to understand and assess a journal article, blog or news story that uses data to support its claim, an ability to use spreadsheet software and to visualise data, etc. Data also lies at the core of much academic research across many disciplines. This course aims to develop ‘number sense’ in students, an instinct in how to translate real world questions into a dataset that they interrogate, analyse and visualise by posing their own research question. This same ‘number sense’ will be applied to analysing published research critically that others have presented and assessing its rigour and judging the veracity of the conclusions drawn. Parts of the course draws on an approach that has been labelled ‘Calling Bullshit’; a willingness to critically evaluate the (mis)use of data both in popular media and in academic writing. Engaging and meangingful examples are placed at the core of the teaching, with past topics including identification of ethics, faked research, research retractions, vaccination debates, data archives and curation, data justice and usefulness of surveys and polls.

This course is designed for students who recognise the importance of exploring the role of data in research. Using problem-solving skills, we all can learn how to recognise patterns and how to interpret data, as well as interrogate published data analysis and recognise both good and bad practice. Students wishing to attend this course must have a willingness to think critically and to engage with data, basic concepts and software. The course will also be of interest for those wishing to digest and present data within their Year 3 Dissertations – whatever the topic and discipline - or thinking about a postgraduate degree.


The course guides students how to record quantifiable data, how to manage data sets, and how to draw conclusions from them. It introduces a range of transferable skills: computer-aided exercises will allow the student to gain valuable spreadsheet and analytical skills, and gain hands-on experience in creating and analysing databases. Student will gain a ‘numbers sense’ and will have an opportunity to put this into practice by critically analysing and reviewing data-heavy publications.

Knowledge and understanding

  • possess a basic knowledge of the issues involved in the recording, handling and analysing of quantitative data in the humanities; be aware of the finite nature of some data, such as archaeology.
  • appreciate some of the potentials and problems of more complex types of statistical analysis;
  • be aware of the ethical ramifications of collecting, analysing, publishing and storing data from a variety of traditional and online sources
  • understand, evaluate and critique data analysis carried out by others
  • have carried out an independent research project

Intellectual skills

  • understand how to formulate research questions
  • understand the relationship between recording and interpretation;
  • understand how to break down information into attributes;
  • abstract knowledge and convert into a quantifiable form;
  • critically evaluate published data analyses;
  • understand the process of data based enquiry and research, including the role of academic review.
  • appreciate the diversity of humanities disciplines and accord respect to diverse views and voices;
  • understand the role that data play in contemporary debates and how data can be used and misused.

Practical skills

  • understand the principles of organising data collection and storage;
  • be able to design and construct databases/spreadsheets;
  • utilise online data sources
  • be able to carry out simple statistical exploration and testing;
  • visualise patterns using relevant software

Transferable skills and personal qualities

  • feel comfortable with dealing with large datasets in any setting
  • translate real world problems into data and vice versa
  • use of archives, spreadsheets, databases and statistics
  • all the skills in the earlier categories are inherently transferable

Employability skills


Assessment methods

Article critique 20%
Independent project 80%


Feedback methods

Feedback method

Formative or Summative

Written feedback

Students will receive summative and formative feedback on their written coursework

Oral feedback

Lectures and tutorials are a place for directed discussion and thus provide verbal formative feedback on the development and presentation of argument and interpretation on a weekly basis. In advance of submitting written coursework, students are encouraged to discuss their plans with the course convenor who will provide formative feedback.


Recommended reading


Best, J. 2001. Damn lies and statistics. Untangling numbers from the media, politicians and activists. Los Angeles, CA: University of California.

Drennan, R., 2009. Statistics for Archaeologists: a Commonsense Approach, 2nd ed.  Springer.

Eddington, D. 2016. Statistics for Linguists: A step-by-step guide for novices. Cambridge Scholars Publishing.

Feinstein, C.H. and M. Thomas 2002. Making History Count: A primer to quantitative methods for historians. Cambridge University Press.

Huff, D. 1954. How to lie with statistics. London: Norton & Co.

Lock, G. and Fletcher, M., 2005. Digging numbers: elementary statistics for archaeologists, 2nd ed.  Oxford University School of Archaeology.

Madrigal, L., 1998. Statistics for Anthropology.  Cambridge University Press.

Sedkaoui, S. 2018. Data Analytics and Big Data. Wiley.

Shennan, S., 1997. Quantifying Archaeology, 2nd ed.  Edinburgh University Press.

Study hours

Scheduled activity hours
Lectures 33
Independent study hours
Independent study 167

Teaching staff

Staff member Role
Stuart Campbell Unit coordinator
Ina Berg Unit coordinator

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