Since 2021 I am teaching two fall courses of the second year of the Master in Collective Intelligence at UM6P.

Programming, Data Science and Statistics 3 (PDSS 3)

This module covers various scientific computing techniques and is designed to equip the students of the MSc programme in Collective Intelligence with different methods useful in computation, modelling and analysis of complex systems. PDSS3 Syllabus

Decision making & Leadership Lab

In this course students learn multiple theories of organizational behavior and everyday leadership and apply them to actual cases of organizational change. We also explore dynamics of individual and group decision making and the challenges of managing motivation, competing interests and emerging ethical issues an increasingly complex and changing world. LL Syllabus

Teaching portfolio

Here I make public a teaching portfolio that summarises a coherent set of materials representing my view of teaching as a scholarly activity, my professional experience and pedagogical training as well as some examples of course design.

Teaching resources

This section contains a variety of materials that I have developed and adapted for my seminars on research methods, agent based models and data collection and processing.

Research methods: Linear models and mixed effects models in R

Stats workshop.

Research methods using R

Lab 1: T-test and Simple linear regression. Download ipynb file

Lab 2: One-way ANOVA. Download ipynb file

Lab 3: Multiple regression. Download ipynb file

Twitter data collection and analysis using R

Lab 4: Data collection and co-ocurrence analysis.

Agent based models

Lab 5: Building a simple agent based model

Lab 6: Generating data using a cultral evolutionary model. In this lab we will use a version of the model published in Cognitive Science: