I teach/taught classes at the University of Zürich, University College London, the University of Essex, King’s College London, Waseda University (Tokyo), and the Essex Summer School in Social Science Data Analysis. I often teach various courses that can be labeled data science. I also offer courses in comparative politics (representation, democratization).
Current Courses:
 Applied Data Analysis for Data Journalists (MA, U of Zürich)
Binary choice models, pseudoBayesian approach to simulating uncertainty, measuring ideal points (IRT), estimating public opinion (MrP & MrsP), machine learning (crossvalidation and regularization).
 Foundations of Public Opinion Research (MA, University of Zürich, 2018, 2019, 2020)
Cotaught with Marco Steenbergen. Course starts with how we can think of opinions and ideology and how individuals answer surveys. Creating survey questions and fielding surveys. Analyzing probability and nonprobability samples.
 Multilevel Analysis (MA, University of Zürich, Spring 2018)
Hierarchical modeling (mixed effects models, multilevel models) for continuous and discrete outcomes and applications in survey research (especially Mr and Mrs P). Complex models in Stan and MC simulation.
Previous Courses:
 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).
 Multilevel Analysis (PhD, Waseda University, Tokyo, Summer 2017)
Hierarchical modeling (mixed effects models, multilevel models) summer school course for PhD students. Models for continuous and discrete outcomes and applications in survey research (especially Mr and Mrs P).

Introduction to Quantitative Research Methods (MSc, UCL, Fall 2014/15/16)
Introduction to applied statistical modeling. Levels of measurement, hypothesis testing, ttests, 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, pseudoBayesian approach to simulating uncertainty, measuring ideal points (IRT), estimating public opinion (MrP & MrsP), machine learning (crossvalidation and regularization). 
Machine Learning (PhD, Summer School Essex, 2016)
Introductory class to statistical learning. Classification approaches, crossvalidation, 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, ttests, 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 quasiexperimental designs.