Social Data Science

专业详情

The growing field of social data science sits at the intersection of data science approaches to information retrieval, modelling, and prediction with social science approaches to theory-driven analysis, critiques of social processes, and linkages between policy and practice. The Social Data Science degree seeks students with training or a demonstrable aptitude for social science work and programming to refine and extend their skills through the generation, analysis, and critique of large-scale social data. The tools for such an approach are multifaceted and evolve quickly. Our programme embeds recent machine learning approaches to prediction, scalable strategies for ingesting and managing large scale data, analytical statistics for explanations, and specialist approaches such as computer vision, natural language processing, and network science. As a social science degree these approaches are generally applied to questions of social scientific relevance such as social inequality, censorship, hate speech, cohesion, and wellbeing.

Students will be expected to spend around 40 hours studying each week during term, and to undertake further study and complete assessments during termly vacation periods. During Michaelmas and Hilary Terms, MSc students are advised to allocate between 10 and 15 hours each week for each course they undertake.

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)

In Hilary term, this equates to:

• 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)

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. The course is primarily taught using the Python programming language with small exceptions for specialist work where necessary.