I teach/taught classes at the University of Essex, University College London, the University of Zurich, and the Essex Summer School in Social Science Data Analysis. I currently teach various courses that can be labeled data science. I am also interested in offering courses in comparative politics (representation, democratization) in the future.
- Measuring Public Opinion (BA, University of Essex, Spring 2017)
Theoretical foundations (ideology, attitudes, cognitive processes), practical aspects of fielding surveys (e.g. coding a new survey), and empirical tools to analyze the raw data (sample correction possibilities).
- Introduction to Quantitative Research Methods (MSc, UCL, Fall 2014/15/16)
Introduction to applied statistical modeling. Levels of measurement, hypothesis testing, t-tests, linear regression, and binary models. Github page for lab sessions: https://uclspp.github.io/PUBLG100/
- Principles of Research Design (BSc, University College London, Fall 2015)
Research design class for first year undergraduate students in Public Health, Geography, and PPE (Philosophy, Politics, Economics). Scientific process, threats to inference (selection, OVB, …), experimental research (laboratory, field, and natural), and research ethics.
- Data Analysis II (BSc, University College London, Spring 2015)
Maximum Likelihood Estimation, choice models, duration models, hierarchical models, hierarchical GLMs, and panel data models.
- Applied Data Analysis for Data Journalists (MA, U of Zürich, Fall 2013/14/15/16)
Binary choice models, pseudo-Bayesian approach to simulating uncertainty, measuring ideal points (IRT), estimating public opinion (MrP & MrsP), machine learning (cross-validation and regularization).
- Machine Learning (PhD, Summer School Essex, 2016)
Introductoray class to statistical learning. Calssification approaches, cross-validation, bootstrap, regularization and model selection, generalized additive models, regression trees, random forests, and unsupervised learning.
- Introduction to Statistics (BSc, Summer School Essex, 2015)
Introduction to applied statistical modeling. Levels of measurement, hypothesis testing, t-tests, linear regression, and binary models.
- Introductory Game Theory (MA, U of Zürich, 2012)
Preferences, Nash equilibrium, simultaneous and sequential games, and mixed strategies.
- Basic and Advanced Statistics for Social Scientists (PhD, CUSO, 2011)
Repetition linear regression, binary & ordinal models.
- Maximum Likelihood Estimation (PhD, U of Zürich, 2010)
MLE theory, Bayesian approach to simulating uncertainty, regression, binary, and ordinal models.
- Research Design (PhD, RRPP, University of Skopje, 2008)
Descriptive and causal inference, experimental and quasi-experimental designs.