MA Course Difficulty Level

Classifications for courses eligible for MA in Social Science Data Analysis

Students can count a maximum of one introductory course toward their MA course requirement. It is compulsory to take a minimum of two advanced courses.

Introductory

Introduction to Quantitative Text Analysis
Introduction to Regression
Designing and Analysing Surveys
Introduction to Game Theory
Longitudinal and Panel Data Analysis
Survey Experimental Design
Introduction to Web Scraping and Data Management for Social Scientists
Introduction to Quantitative Methods in R
Qualitative Data Analysis: Methodologies for Analysing Text and Talk
Qualitative Interviewing
Methods For Field Work in Social Science
Ethnography and Ethnographic Methods

Intermediate

Introduction to Social Network Analysis
Multilevel Statistical Models for the Social Sciences Using Stata
Categorical Data Analysis
Introduction to Applied Bayesian Statistics
Quantitative Data Analysis and Statistical Graphics with R
Introductory to Programming in Python for Social Scientists
Multilevel Models: Practical Applications
Quantitative Text Analysis
Longitudinal Data Analysis
Mixed Methods Research
Causal Inference and Experiments in the Social Sciences
Quantitative Methods for Causal Inference and Policy Evaluation
How to Communicate and Engage Using Data Analysis in R
Doing Discourse Analysis: Populism, Neoliberalism and Radical Democratic Politics
Applying Discourse Theory – Politics, Ideology, Populism

Advanced

Machine Learning for Social Scientists
Spatial Econometrics
Confirmatory Factor Analysis and Structural Equation Modelling
Programming and Simulation Methods for Computational Social Science
Machine Learning For Estimating Treatment Effects From Observational Data
Data Visualisation with R: Explore, Model and Communicate Social Data Analysis
Machine Learning for Tabular Data
Bayesian Analysis for the Social and Behavioural Sciences
Advanced Methods for Text as Data: Natural Language Processing
Ideal Point Estimation, Item Response Theory, and Scaling Methods for Surveys and Behaviour
Deep Learning For Text and Vision