Skip to main content
Full time — Closed
Graduate

MSc in Social Data Science

The multidisciplinary MSc in Social Data Science welcomes students with an interest in applying quantitative and computational methods to questions of social and political significance for academics, policymakers and the public. 

Closed: Full time

Closed to applications for entry in 2026-27. Register to receive an email when applications open (for entry in 2027-28). 

Apply now
Expected length:
  • Full time: 10 months
Expected start date:
  • Full time:
English language level:
  • Higher level required
Someone typing on a laptop

Image credit: University of Oxford Images / Oxford Atelier

About the course

The course is designed for students with some familiarity with programming and a strong background in social sciences, although applications are welcomed from all disciplinary backgrounds who meet the course entry requirements.

With the rapid expansion of big data and artificial intelligence (AI) in society there is a need both to understand how to best make use of these tools, as well as to consider their social implications from a practical and grounded perspective. This is an applied programme that combines machine learning, multivariate statistics, mixed-methods research, and a substantive focus on social, ethical, and legal considerations for AI and data broadly and for the governance and regulation of the internet more specifically.

It is an ideal course for ambitious students at the intersection of computing and the social sciences who are seeking careers with data in the public, private, and non-profit sectors.

You will join a cohesive cohort and will be expected to dedicate around 40 hours to this course each week during term, and to undertake further study and complete assessments during termly vacation periods. The OII's busy calendar of seminars and events showcases many of the most noteworthy people in internet research, innovation and policy, allowing you to engage with the cutting edge of scholarship and debates around internet technologies and AI.

Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, students continuing on to doctoral study have the option of taking a part-time DPhil.

Course structure

This section provides an overview of the course structure, while details of the individual course components are provided below.

During the first term, you will receive core training in data science and machine learning, research design, and social science theory. 

In the second term, you will receive further core training in applied social science theories and applied analytical statistics. You will also take two option courses of your choosing. 

The third term is centred around the thesis, supported by cohort seminars and individual supervision. This independent research project of up to 12,000 words allows you to apply your learning to a topic of your choice. 

During Michaelmas and Hilary terms, the study commitment equates to roughly 10 and 15 hours each week for each course taken.

In the first term (Michaelmas), this includes:

  • At least 20 hours per week on reading, preparation and formative assignments
  • 16 to 20 hours per week in classes (typically one and a half to two hours of lectures daily, one and a half to two hours of tutorials and practical exercises three to four days a week, plus additional seminars or workshops on certain courses)

In the second (Hilary) term, this includes:

  • At least 24 hours per week on reading, preparation and formative assignments (6 hours for each core/option course)
  • Ten to 12 hours per week in classes (typically one and a half to two hours of lectures per course, plus a one-hour seminar or workshop on certain core and methods-based courses)

Core components

You will take five core courses and write a thesis.

Option modules

You will take two option modules during the second term of the year. 

Course details

Entry requirements

For entry in 2026-27

Funding and costs

College preference

Before you apply

Completing your application

Contact details