Course summary

The information provided on this page was correct at the time of publication (November 2023). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas. 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 program 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. The MSc in Social Data Science is primarily assessed by essays that apply these methods to a substantive research question. This involves motivating the question with domain-level academic expertise and motivating the analysis with an understanding of the potential and limits of specific (usually computational) methodologies. The three term MSc 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 formal requirements. The course is administered by the Oxford Internet Institute, a department within the Social Sciences Division. Teaching and supervision faculty are drawn from the department as well as a variety of departments around the University including but not limited to Engineering Science, Mathematics, Linguistics, Statistics, and Sociology. 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. During Michaelmas and Hilary terms, this 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 (ten hours for the intensive course, five hours for each of the two foundation courses)

• 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-four days a week, plus additional seminars or workshops on certain courses)

• 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)