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
Introduction to Statistics for Survey Data Analysis
Introduction to Web Scraping and Data Management for Social Scientists
Introduction to Quantitative Methods in R
Introduction to Probability Theory for MLE, Bayesian Inference and Machine Learning Using R
Introduction to Statistics for Social Science Research with SPSS
Introduction to Game Theory

Intermediate

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

Advanced

Advanced Survey Data Analysis and Survey Experiments
Machine Learning for Social Scientists
Spatial Econometrics
Confirmatory Factor Analysis & Structural Equation Modelling
Advanced Methods for Social Media & Textual Data
Computational methods for Social Data Science: Exploring Society through Visualization and Modelling
Bayesian Analysis for the Social and Behavioural Sciences
Heterogeneity and Dynamics: Time Series and Panel Data
Scaling Methods and Ideal Point Estimation for Surveys and Behaviour
Advanced Methods for Text as Data: Natural Language Processing
Advanced Machine Learning: Deep Learning Models