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DSI Undergraduate Modules

At the DSI, our teaching is grounded in the belief that data-driven approaches are essential to rigorous social science research. Our courses equip students across the School with skills, tools and critical insights to engage with data in meaningful, responsible and impactful ways, so that they can apply data science techniques and insight to their field of research.

As well as confidently harnessing the power of data in academic work, we want our students to develop a critical understanding of the evolving digital world, so that they can interrogate the ethical, political and social dimensions of emerging technologies.

DS101 – Fundamentals of Data Science

AI can grade your exams, recommend your Netflix binges, diagnose your illnesses, and only occasionally floods the internet with misinformation. What could possibly go wrong?

This course is designed to introduce students to data science and its practice: how it works and how it can produce insights from social, political, and economic data. Using real-world case studies like the Ofqual fiasco, biased medical AI, and generative deepfakes, students will unpack the hits and misses of data science in social, political, and economic contexts – exploring how it works, how it produces insights, and where it can go wrong.

  • course study period: Autumn Term
  • requisites: Designed for students across any social science degree programmes. Students with little or no experience in computer programming are welcome
  • assessment type: group presentation (30%) and coursework (70%)
  • more information:
    DS101 course GitHub page
    DS101A (Autumn Term) course guide

DS105 – Data for Data Science

How do birds migrate in the UK? Why does living geographically close to central London sometimes mean a longer commute than you’d expect? If Pokémons were real, where would they live on planet Earth?

These are just a few of the creative questions tackled by past DS105 students using real-world data. This course will cover the fundamentals of data, with an aim to understanding how data is generated, how it is collected, how it must be transformed for use and storage, how it is stored, and the ways it can be retrieved and communicated.

In this module, students will master reproducible data analysis with Python – and will have the freedom to select their own topics to research, using the skills learnt on this module to turn their questions into insights.

DS202 – Data Science for Social Scientists

What do central banks, fake news, and Truth Social rants have in common? They’re data, waiting to be mined.

This module extends the foundation of probability and statistics with an introduction to the most important concepts in data science and applied machine learning, with social science examples. Students will get a hands-on introduction to the most fundamental machine learning algorithms, learn how to assess their performance using key metrics, and explore how these models shape decision-making in real-life scenarios.

  • course study period: Autumn Term (where the programming language taught will be R) or Winter Term (where the programming language taught will be Python)
  • requisites: Students must have A-level maths or equivalent, and basic programming knowledge, preferably in Python or R, is highly recommended
  • assessment type: Coursework (60%) and group project (40%)
  • more information:
    DS202 course GitHub page
    DS202A (Autumn Term) course guide
    DS202W (Winter Term) course guide

DS205 – Advanced Data Manipulation

Do you want to contribute to a real project, solving real data needs as you learn to handle complex data pipelines? This might be the course for you.

This module offers students a hands-on learning experience where theory meets practice in sustainability research. The primary objective of this module is to equip students with the skills to collect and manage ‘real data’ in a computationally efficient manner. Delivered in collaboration with the Global Climate Transition Centre (TPI Centre) at LSE, the course emphasises practical learning using Python and provides live coding demonstrations during all lectures and seminars. With the use of such a programming tool, the module teaches students advanced data manipulation techniques for unstructured data such as text and the use of natural language processing methods such as APIs to process text using AI tools and large language models.

Note: Students will need to demonstrate knowledge in programming by having completed courses such as Data for Data Science (DS105), Programming for Data Science (ST101) or alternatively summer school training courses such as Data Engineering for the Social World (ME204) and Macroeconomics I (EC1B1). Should you have any other training that demonstrates a knowledge in programming, please contact the DSI to verity its suitability as a prerequisite. Contact DSI.Ug@lse.ac.uk.

Learn more the different modules

Find out which of our four undergraduate data science modules is right for you.

Key contact
Got a question about the DSI? Get in touch with our Teaching and Assessment Administrator who will be happy to help current students at any stage of their LSE journey.

Events
In addition to opportunities to study, the DSI offers an events programme to give LSE students the chance to benefit from world leading multidisciplinary expertise. These events include seminars, workshops, public lectures and careers networking sessions.