Teaching
Since 2021, I have taught two fall courses in the second year of the Master in Collective Intelligence at UM6P: Programming, Data Science and Statistics 3 and Decision Making & Leadership Lab, and since 2026 at the Vanguard Centre I also teach in the module Critical Thinking and Causal Reasoning. My teaching combines computational, statistical and social-scientific approaches to help students analyse complex systems, reason critically about data, and apply modelling and decision-making tools to real-world problems.
Teaching resources
This section contains teaching materials that I have developed, used and adapted for courses and workshops on programming, data science, statistics, research methods, statistical reasoning, decision making, leadership, and computational approaches to the study of complex social systems.
My teaching has developed across more than 600 hours of university-level instruction, including regular postgraduate teaching at UM6P since 2021. Across these materials, I combine theoretical foundations with practical laboratories, live coding, data analysis, modelling exercises, case-based discussion, and critical reflection on the use of digital tools and artificial intelligence in research and decision making.
Programming, Data Science and Statistics 3
Programming, Data Science and Statistics 3 (PDSS 3) is a module for the MSc programme in Collective Intelligence. The course introduces scientific computing techniques useful for statistics, computation, modelling, and the analysis of complex systems.
The module combines practical programming with core tools from data science and statistical reasoning. Topics include reproducible scientific code, data import, cleaning, transformation and visualisation, statistical inference and uncertainty, regression, mixed-effects models, non-linearity, equation-based models, prediction, classifiers, basic neural-network architecture, simple ordinary differential equations, and communication of results through figures, reports and notebooks.
Repository | PDSS3 Syllabus | Download course files | Linear models and mixed-effects models in R
Decision Making & Leadership Lab
Decision Making & Leadership Lab introduces students to theories of organisational behaviour and everyday leadership, with applications to real cases of organisational change. The course explores individual and group decision making, motivation, competing interests, cooperation, conflict, negotiation, coalition formation, social dilemmas, free-rider problems, tragedy of the commons, uncertainty, and ethical challenges in complex organisational settings.
The course typically combines short lectures, guided discussions, case analysis, in-class activities or simulations, and written briefs or presentations.
Statistics workshop
Stats workshop contains materials from a statistics workshop for PhD students at the School of Collective Intelligence, held from 5 to 7 June 2022. The repository includes a notebook on linear models and linear mixed-effects models in R, together with example data.
Repository | Linear models and mixed-effects models in R | Example dataset
Statistical Mistakes – Teaching Sessions
Statistical Mistakes – Teaching Sessions is a module designed to help students identify, understand, and avoid common statistical mistakes in empirical research. The emphasis is on critical reasoning rather than the mechanical application of methods.
The sessions cover common problems such as confusing correlation with causation, inadequate control groups, invalid group comparisons, misinterpretation of statistical significance, small sample sizes, low statistical power, non-independence of observations, violated model assumptions, spurious correlations, and the difference between statistical and practical importance.