2020 Course List for ESS MA in Social Science Data Analysis.

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
Introduction to Quantitative Methods in R
Introduction to Statistics for Social Science Research with SPSS
Introduction to Programming in Python for Social Scientists
Web Scraping and Data Management for Social Scientists
Introduction to Statistics for Survey Data Analysis with SPSS

Intermediate
Introduction to Social Network Analysis
Introduction to Multilevel Models
Longitudinal Data Analysis
Beyond OLS: Categorical, Choice, and Count Models
Data Analysis and Programming in Stata
Quantitative Data Analysis and Statistical Graphics with R
Multilevel Models: Practical Applications
Mixed Methods Research
Panel Data Analysis for Comparative Research
Causal Inference and Experiments in the Social Sciences
Generalized Linear Models
Introduction to Applied Bayesian Statistics
Quantitative Text Analysis
Quantitative Methods for Causal Inference and Policy Evaluation

Advanced
Confirmatory Factor Analysis and Structural Equation Modeling
Advanced Survey Data Analysis and Survey Experiments
Advanced Methods for Social Media & Textual Data
Machine Learning for Social Scientists
Maximum Likelihood Estimation
Bayesian Analysis for the Social and Behavioural Sciences
Spatial Econometrics
Advanced Quantitative Methods
Time Series and Panel Models for Dynamic and Heterogeneous Processes
Scaling Methods and Ideal Point Estimation: Measuring Patterns and Preferences
Advanced Machine Learning for Social Scientists
Computational Methods for Social Data Science: Exploring Society Visualisation and Modelling
Advanced Methods for Text-as-Data: Natural Language Processing