Social Sciences with Data Science, BA (Hons)

This is general information about the course. For application-specific course materials and application information, please click HERELink opens in a new window.

Social Sciences with Data Science (SSDS) combines social inquiry with data analytics. This course is multidisciplinary, including subjects such as Sociology and Economics, with options in Politics and International Studies and Interdisciplinary Methodologies, among others. It is driven by offering a comprehensive understanding how data can be obtained, managed, and analysed to better explain social, economic, and human processes in contemporary societies.

This programme asks key questions such as: how can data help to explain complex social and economic processes? What is good data, and how can it be collected? How can data be effectively analysed and presented? The programme encourages a critical understanding of the role of data in data driven societies, by developing students’ skills in data collection, analysis, and presentation across disciplines. Studying this multidisciplinary programme, focusing on data skills, will enable students to make sense of data and to work with data towards solving social and economic problems.

Drawing on the existing strength within the Department of Sociology and Department of Economics, as well as across the Faculty of Social Science unique aspects of this programme will be multidisciplinary learning, hands-on teaching, research opportunities on working with data, and placement opportunities. Students will explore areas such as: techniques of data collection, including web surveys, text data, and social media data; approaches to data analysis using software employed in data science; ethics of data collection and analysis, including data justice and algorithmic fairness; applied data analysis, including questions of economic and social development, as well as economic and social inequalities.

The programme comprises a work placement opportunity, where students apply their skills to solve real-word problems in an employer project. A multidisciplinary focus will give a thorough, yet broad understanding of issues, key concepts, approaches, as well as challenges of data-driven societies.

Course Structure

Core Modules

Year 1	Term 1	Researching Society and Culture [SO120Link opens in a new window]
		Economics 1 [EC107Link opens in a new window] Mathematical Techniques A [EC139Link opens in a new window] or Mathematical Techniques B [EC140Link opens in a new window]
		One Optional Module from Dedicated List
	Term 2	Introduction to Social Analytics in Social Inequalities Research [SO130Link opens in a new window]
		Economics 1 [EC107] Statistical Techniques A [EC122] or Statistical Techniques B [EC124Link opens in a new window]
		One Optional Module from Dedicated List
Year 2	Term 1	Web Survey Design and Data Collection [SO2H4Link opens in a new window]
		Applied Econometrics [EC203Link opens in a new window] or Econometrics 1 [EC226Link opens in a new window]
		Two Optional Modules from Dedicated List
	Term 2	Survey Data Analysis and Reporting [SO2H3Link opens in a new window]
		Applied Econometrics [EC203Link opens in a new window]or Econometrics 1 [EC226Link opens in a new window]
		Two Optional Modules from Dedicated List
Year 3	Term 1	Dissertation [SO301Link opens in a new window] Optional Core Module#
		Two Optional Modules from Dedicated List
	Term 2	Dissertation [SO301Link opens in a new window] Optional Core Module#
		Two Optional Modules from Dedicated List

# In Year 3 students choose two out of seven optional core modules: (1) Social Data Science [SO365Link opens in a new window]; (2) Data Science for Economists [EC349Link opens in a new window]; (3) Generative AI: Histories, Techniques, Cultures, and Impacts [IM354Link opens in a new window]; (4) Scaling Data and Societies [IM350Link opens in a new window]; (5) Data Science Across Disciplines: Principles, Practice and Critique [IM339Link opens in a new window]; (6) Computational Modelling and Simulation [IM356Link opens in a new window]; (7) Spatial Data: Mapping & Power [IM355Link opens in a new window].

Optional Modules (Examples)

Year 1	Year 2	Year 3
The World Economy: History & Theory (Full year, 30 CATS), Economics History of Sociological Thought, Sociology Class and Capitalism in the Neoliberal World, Sociology Life of Media: Past, Present and Future, Sociology	Political Economy: Theory and Applications, PAIS Commercial Cultures in Global Capitalism, Sociology Relationship and Family Change: Demographic and Sociological Perspectives, Sociology Experimental Economics, Economics Development Economics, Economics Introduction to Causal Inference in Quantitative Political Analysis, PAIS	Labour Economics, Economics Research in Policy Evaluation, Economics Experiments in the Social Sciences and Humanities, Sociology Sociology of End Times, Sociology Postcolonial Theory and Politics, Sociology Determinants of Democracy: Analysing Emergence, Survival, and Fall, PAIS

Placement Opportunities (Credited Optional Modules)

Three 3^rd Year Placement Modules

(1) Numbers in the Workplace [QS305]

This module includes a 4-week (summer) placement which renders students' degree professionally relevant, enabling students to develop real world employability skills and apply data analysis skills in a non-academic setting. Students are assessed through a reflective essay and a poster on their placement experience and development of their professional skills. By the end of the module, students should be able to: Develop a range of transferable professional skills for future employment (e.g. CV design, job application, cover letter. etc.); develop a critical understanding of the role of data analysis in the workplace; develop professional practice with clients; become familiar with key terminology and soft skills associated with acting as a consultant in quantitative analysis.

(2) Employer Project for the Social Sciences [SO372]

This module includes training components (workshops) at the University on writing a tailored CV and cover letter, internal selection application with feedback provided to students, a remote placement with employers offering a structured remote project with weekly meetings, participation in research & teams meetings and the possibility (logistics permitting) to spend a day on-site at the organisation. At the end of the project students are assessed through a reflective essay and a poster on their placement experience and development of their professional skills.

(3) Work Placement [SO373]

This module includes training components (workshops) at the University, a self-sourced placement with employers on degree-relevant sectors and assessments through a reflective essay and poster presentation on the placement experience and acquired professional skills. This module starts during the Term 2 of Year 2 with a workshop on writing CV & Cover Letter; practice on writing applications with feedback and preparation for the workplace. Students take placements over the summer and enrol on the module in Year 3, with the assessments (reflective essay and poster) during Term 1.