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Course Content

The Applied Social Data Science programme offers a wide range of modules that aim to introduce students to state-of-the-art quantitative methods and social science research skills. Lecture and seminar based modules are offered and assessment is based on assignments and exams. The number of modules on offer and topics covered, and whether there is any choice of module topics, varies from year to year depending on student numbers. For 2023/24 students will also need to choose 3 out of 4 optional modules. Students are expected to bring their own laptop (Mac/Windows/Linux) for use in seminars and tutorials throughout the course; note - tablets are not suitable. For minimum laptop specs, please see this helpful guide

Michaelmas Term

  • Computer Programming for Social Scientists (10 ECTS)

    Students will be introduced to Python and R, two fundamental data science programming languages. This will be a core module that later modules will build upon. Basic and intermediate programming skills will be introduced. Familiarity with core programming concepts will be covered: e.g., functions, variables, conditions, loops, data structures, working with libraries, interacting with APIs. Students will engage with the material in a hands-on environment, which will include coding homework/exercise throughout the module. Knowledge will also be assessed with a comprehensive exam at the end of the module.

  • Applied Statistical Analysis I (10 ECTS)

    This module introduces the core concepts of quantitative social science research. The module begins with a thorough review of probability theory. It then takes a step by step approach, including description of simple and more complex data, to issues of random samples, to the types and requirements of statistical inference, and finally to linear statistical models. The module provides a solid foundation for further training in statistical modeling, in particular “Applied Statistical Analysis II”. The module will be based on the software package R. Further, the module will introduce core methods in data visualization using R for a range of data: univariate, bivariate, time-series, network, etc. Best practices in visualizing data such as tailoring to the target audience and accessibility concerns will also be introduced.

  • Research Design for the Social Sciences (10 ECTS)

    This module will cover crucial aspects of the social scientific research process, with a particular focus on causal inference. Students will become familiar with core concepts of the scientific method: conceptualization, theory development, falsifiability, causality, hypothesis testing, and operationalization. Experimental and quasi-experimental design will also be covered. Students will be assessed on their ability to critically consume and produce social scientific research designs. Specifically, students will rely on research design and scientific method concepts to evaluate a paper presented at a recent international research conference via a peer-review report. Further, a research proposal will be required which covers core research components: identification of a research puzzle, concise review of literature, theoretical development, conceptualization and operational definition of concepts, as well as a description and justification of methodological approach.

Hilary Term

  • Applied Statistical Analysis II (10 ECTS)

    This module will cover important methods from statistics for the applied data analyst. Students will learn to apply core methods in R such as linear regression modeling and limited dependent variable methods to answer social science questions. Students will also learn how to statistically model quasi-experiment designs such as difference-in-differences, regression discontinuity and panel regression. This will be a project-based module that allows the student to become familiar with the quantitative social science workflow.

  • Introduction to Machine Learning (5 ECTS)

    Introduction to Machine Learning is designed to offer an introduction to the basics of ML, specifically with a hands-on curriculum aimed at developing knowledge and skills in establishing ML pipelines with state of the art languages and toolkits. This module is designed for students with limited prior experience of programming. It will introduce the fundamentals of programming, with a focus on setting up an effective pipeline for processing datasets to execute common ML techniques such as Scalable Vector Machines and Linear Regression.

  • Social Forecasting (5 ECTS)

    This module will cover the fundamental techniques and approaches to forecasting. Students will in particular learn the main building blocks of a forecasting model, how to build their own forecasting models, and how to evaluate their performance. Techniques ranging from ARIMA models to neural networks will be applied to both time series and panel data. Students will be able to apply the approaches covered in the module to forecast political, economic, and/or social outcomes in a research report.

  • Quantitative Text Analysis for Social Scientists (5 ECTS)

    This module focuses on a range of computational tools—stemming from the fields of machine learning and natural language processing (NLP)—that are essential for large-scale analyses of text information. The aim is to provide students with a hands-on introduction to collecting, processing, and analyzing “text-as-data” for the purpose of answering important social science research questions. The module will also cover corpus acquisition methods as well as social media research applications. Students will apply these skills to produce a state-of-the-art research report based on a novel collection of text documents and meta-data.

  • Special Topics in Applied Machine Learning (5 ECTS)

    This module will extend the content covered in Introduction to Machine Learning, by focusing further on areas such as model evaluation, as well as introducing students to practical usage of recently developed supervised and unsupervised machine learning algorithms. The core emphasis of the module is the effective application of ML methods to study social scientific research questions. Students will apply these methods in a capstone research project at the end of the module.