Prof Melissa Sands

This course is available on the MPA in Data Science for Public Policy, MSc in Development Management (Political Economy), MSc in Development Studies, 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. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

How to apply: To apply for a place on this course, please submit a short statement of no more than 200 words via LfY. Your statement should outline your reasons for applying and explain how the course will support your academic and/or career goals. If applicable, you should also demonstrate how you meet any pre-requisites listed in the course guide. If the course is compulsory for your programme, no written statement is required.

Priority for places on Government (GV) courses will be given first to students for whom the course is compulsory. Where applicable, priority will then be given to students on the Department of Government programmes listed in the ‘Availability’ section of the course guide, followed by students on other programmes listed in that section. Students on all other programmes will be considered next, where a course is available as an outside option. You should ensure that you meet any pre-requisites listed in the course guide before applying. Places on capped courses cannot be guaranteed. Where a course is oversubscribed, places will be allocated based on programme priority and the written statement.

Deadline for applications: The deadline for applications is 12:00 noon on Friday 25 September 2026. You can expect to be informed of the outcome of your application by 12:00 noon on Monday 28 September 2026. 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 2 groups. Priority will be given to students enrolled on programmes in the Department of Government.

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.

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.

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.

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.

• 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.

Course participation (15%).

Project (85%, 3000 words) in Spring Term Week 2.

The coursework/project comprises a replication exercise, where students replicate and extend the analysis of one published research paper.

Course participation will include seminar participation and a presentation in the WT.