GV4L3 Half Unit
Data Science Applications in Politics and Policy
This information is for the 2025/26 session.
Course Convenor
Prof Melissa Sands
Availability
This course is available on the MPA in Data Science for Public Policy, MSc in Development Management (Political Economy), MSc in Political Science (Conflict Studies and Comparative Politics), MSc in Political Science (Global Politics), MSc in Political Science (Political Behaviour), MSc in Political Science (Political Science and Political Economy) and MSc in Public Policy and Administration. This course is freely available as an outside option to students on other programmes where regulations permit. It does not require permission. This course uses controlled access as part of the course selection process.
How to apply: to apply for a place on this course, please write a short statement of 200 words (max) outlining the specific reasons for applying and how the course will benefit your academic/career goals. Priority will be given to Department of Government students, and then students on the other programmes listed in the 'availability' section of the course guide. You should check that you meet any pre-requisites in the course guide before applying (where applicable). Places on capped courses cannot be guaranteed.
Deadline for application: The deadline for applications is 12:00 noon on Friday 26 September 2025. You can expect to be informed of the outcome of your application by 12:00 noon on Monday 29 September 2025. Any places remaining after this date will be allocated based on priority and written statement - up until course selection closes.
For queries contact: gov.msc@lse.ac.uk
This course is capped at 1 group. Priority will be given to students enrolled on programmes in the Department of Government.
Requisites
Co-requisites:
Students must complete GV481 or MY451A or PP402 or PP455 either before taking this course or in the same year as this course.
Additional requisites:
Basic familiarity with R is recommended.
Course content
This course introduces students to the latest empirical research and covers different applications of novel and “big" data in political science and policy. Themes include causality and credibility, administrative and open data, generative Artificial Intelligence (AI), social media, geospatial data, and text and image data. Students will be introduced to the set of questions that each type of data can help answer. The course situates the “big data” revolution within the broader context of political science and policy research and discusses some of the promises and pitfalls of digital innovations and new data science methods.
Teaching
15 hours of lectures and 15 hours of seminars in the Winter Term.
This course has a reading week in Week 6 of Winter Term.
Formative assessment
Presentation
Problem sets
Students will be expected to produce 1 presentation and 1 problem set in the WT.
The presentation will be a brief (10-15 minute) overview and critique of one published research paper of the student's choice, selected from a menu of options.
Indicative reading
- Brady, Henry E. "The challenge of big data and data science." Annual Review of Political Science 22 (2019): 297-323.
- Titiunik, Rocío. "Can big data solve the fundamental problem of causal inference?." PS: Political Science & Politics 48, no. 1 (2015): 75-79.
- Carlitz, Ruth D., and Rachael McLellan. "Open Data from Authoritarian Regimes: New Opportunities, New Challenges." Perspectives on Politics 19, no. 1 (2021): 160-170.
- King, Gary, Jennifer Pan, and Margaret E. Roberts. "How the Chinese government fabricates social media posts for strategic distraction, not engaged argument." American political science review 111.3 (2017): 484-501.
- Chen, M. Keith, and Ryne Rohla. "The effect of partisanship and political advertising on close family ties." Science 360, no. 6392 (2018): 1020-1024.
- Nickerson DW, Rogers T. 2014. Political campaigns and big data. Journal of Economic Perspectives 28(2): 51–73
- Lerman, Amy E., and Vesla Weaver. "Staying out of sight? Concentrated policing and local political action." The ANNALS of the American Academy of Political and Social Science 651, no. 1 (2014): 202-219.
- Vomfell, L., Stewart, N. Officer bias, over-patrolling and ethnic disparities in stop and search. Nat Hum Behav 5, 566–575 (2021).
- Law, Tina, and Joscha Legewie. "Urban data science." Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource (2015): 1-12.
Assessment
Project (100%, 3000 words) in Spring Term Week 1
The coursework/project comprises a replication exercise, where students replicate and extend the analysis of one published research paper.
Key facts
Department: Government
Course Study Period: Winter Term
Unit value: Half unit
FHEQ Level: Level 7
CEFR Level: Null
Keywords: Political Science, Political Policy, Data Science, Public Policy
Total students 2024/25: Unavailable
Average class size 2024/25: Unavailable
Controlled access 2024/25: NoCourse selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Self-management
- Problem solving
- Communication
- Application of numeracy skills
- Specialist skills