GV223-5-SP-CO:
Methods of Social Data Science
2025/26
Government
Colchester Campus
Spring
Undergraduate: Level 5
Current
Monday 12 January 2026
Friday 20 March 2026
15
14 April 2025
Requisites for this module
(none)
GV222
(none)
(none)
GV222
BSC LL20 Politics with Data Science
This module presents Methods of Social Data Science at an undergraduate level. The course is intended as an overview of the different statistical methods ranging from experimental designs to quasi-experimental designs. A brief introduction to the basics of Data Science and Machine Learning techniques will also be provided. Students will develop the skills to critique methods used in recent academic work and to begin to apply these methods in their own research.
The course will start by introducing causality in social sciences, potential outcomes framework, experiments, selection on observables/matching, difference in differences, and regression discontinuity design. This course goes into more detail on these topics but also briefly introduces the basics of Data Science and Machine Learning techniques.
The topics covered are extensive, and we expect this course to be an introduction for students to then explore the different topics/applications on their own.
The aims of this module are:
• Students to have a deeper understanding of Methods of Social Data Science in political science by introducing causality in social sciences, potential outcomes framework, experiments, selection on observables/matching, difference in differences, and regression discontinuity design.
• To gain an understanding of the basics of Data Science and Machine Learning techniques.
• To familiarise themselves with the interpretation and presentation of empirical evidence in political science.
The module will be particularly useful for students who aim to pursue careers in academia or in research-intensive environments, for example think tanks, research- related government posts, data science, or survey analytics.
By the end of this module, students will:
1) Be able to choose and implement an appropriate statistical model commensurate with their theory and appropriate for a given dataset and research question
2) Be able to improve upon, statistical analyses and their interpretations in leading political science journals have practical experience with conducting high-quality quantitative political science research as well as with the implementation of advanced regression models master the mathematics and statistical theory underlying potential outcomes framework, experiments, selection on observables/matching, difference in differences, regression discontinuity design, and similar methods.
3)Understand the assumptions underlying a variety of statistical models and be able to diagnose violations of these assumptions and present and interpret statistical results effectively.
Students are expected to do the readings before the class, be prepared for programming demonstration, and be ready to ask questions during the class. The course assignments involve a significant amount of programming exercise. Thus, clearly understanding the instructor’s code every week is crucial for students' success in this module.
This module will be delivered over 2 hours per week.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Yes
Yes
No
No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.
Government
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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