During each term, you will be able to select 3 methods from across the qualitative and quantitative methods options, of which one must be quantitative, and one must be qualitative. These electives will allow you to shape the direction of your learning by allowing you to build on existing skills or explore completely new methods.
Thinking Through Writing
What do you do when you write? You think on paper (digitally or otherwise). Writing up thoughts in the form of an argument results in better communication. Writing can be done in different ways, so different styles are often associated with different forms of thinking and doing. Thinking (writing) like a philosopher is not the same as thinking (writing) like a poet, or an architect for that matter.
In this module, we ask questions of interdisciplinarity by examining interdisciplinary thinking in the context of writing. We do this by focusing on one interdisciplinary kind of writing: manifesto writing. Some manifestos have the power to mix styles much in the same way as cross-disciplinary knowledge has the power to make us see things in new ways.
Social Science Research Methods
This module is a deep dive on how qualitative methods are used in research projects. Focussing on social problems, students discover how past researchers carried out their projects by looking at a set of classic and current case studies in social science.
From this, students gain a comprehensive understanding of the benefits and limitations of qualitative research. This helps students later on, when they gain hands on experience of managing their own qualitative study by collecting data via interviews, focus group discussion, and participant observation.
This module allows students to take advantage of the vast array of information that is now available online. Students are supported to pursue their own learning and use the knowledge and skills that they gain to address a complex, real-world problem of their choice.
At the end of the module, students produce two reports of their work. One is aimed at a policy or professional audience - people who may be able to take specific steps to address the problem. The other is aimed at an academic audience, who may be interested in more general approaches to the problem that the student has investigated.
Intermediate Quantitative Analysis: Investigating the Physical World
Starting with a recap of pre-calculus ideas, students develop an understanding and fluency in the use of single variable calculus techniques (the branch of mathematics that deals with the study of functions and their rates of change), which include a mixture of analytical, numerical, and computer algebra tools.
Students model real-world problems in the form of differential equations. Example questions include modelling disease spread, climate, and energy. The end of the module is devoted to student-led projects culminating in a computational essay.
Telling the Story of a Wicked Problem
In the world where everyone is competing for your attention, how can we tell the story of a complex issue like climate change? This module looks at the forms and structures which storytellers use - from a five-act play to a podcast and on to a Virtual Reality platform.
Along the way we consider the business models which support today’s media organisations. At the end of the module, students put it all together an authentic multi-media campaign strategy which draws on everything they have learnt.
Further Statistics and Probability
Real-world data is ‘messy’ with no obvious patterns. This module, therefore, builds on the foundations of first year quantitative methods at LIS to explore a range of more advanced statistical and probabilistic methods for real-world problems.
Students go deeper into the mathematical theory behind well-known techniques, particularly focussing on the ‘captain’ of the module – Bayes' Theorem. Quantitative confidence in applying these methods on big data is built through regular application of coding in Python.
Visual Methods: From Documentation to Transformation
This module provides students with methods used to collect, understand, interpret and create images. Students will explore how images create meaning – images do not exist in isolation – by gathering visual information (recollection methods), creating an archive (analytic methods) and via their own creative research.
Students develop skills in camera journalling (visual diaries), photogrammetry (the science of making reliable measurements through photographs), archival practices, collage, and creative research. This toolkit enables students to craft their own visual narratives: communicating and interpreting the visual aspects of their wider world.
Introduction to Natural Language Processing (NLP)
How does your phone predict the word you’re going to use next? How might we decode an alien signal if we received one? How can we figure out when documents share topics and when they don’t? This module teaches students how to analyse language at speed and scale using libraries in the Python programming language.
Students learn how to obtain large samples of language data and extract non-obvious insights from them. They are also exposed to the basic principles of machine learning with language. At all points, students are encouraged to use NLP in an interdisciplinary way to add to their learning on other modules.
Design thinking is a problem-solving approach with the intention to improve products. But design thinking has not developed in insolation - it was born from best practices in experimental design from the sciences, creativity from the arts, prioritisation from business etc., all filtered through a human-centred lens.
Students will study the structures of common design processes as well as ideation techniques (coming up with ideas and concepts), before the implementation of their own project.
This module uses the computer as a powerful tool to implement and explain the ideas we need for sound conclusions from noisy and complex data. Students add to their knowledge from their first year to build computational models that allow us to explain relationships with data, and to predict new data.
Students will focus on “machine-learning” approaches to data as they are increasingly dominant across academia, industry, law and government. Students learn to understand these methods in order to understand and criticise their output, and to build effective models of their own.
Materials and Making
The digital age removes contact with the physical environment. Lost in cyber space? Craving re-connection and re-education of all things material and grounded?
This module involved entails learning about the world in a tangible and concrete way, via materials and making. The module covers topics ranging from the history of our elements, traditional making methods, cultures of making, and food and agriculture as materials in the world.