Schedule

ODISSEI-SICSS is split into two phases. During the first week the participants will be introduced to ODISSEI and taught machine learning in python. This will be primarily done through a course run by the eScience center. They will directly apply this knowledge to worked examples using a consolidated version of the LISS Panel data, with the aim of predicting fertility behavior in the LISS Panel. On Thursday, the participants will be given additional training on analytical methods that they could apply within the benchmarking challenge. On Friday, the participants will work in teams to submit their best prediction model to a benchmarking platform, where their model will be evaluated by the ODISSEI benchmarking team.
At the end of most days of the summer school, there will be a guest speaker who will present their work or a general topic in computational social science to the participants. These talks will last 20-30 mins with 15-20 mins of Q&A.
Slides will be added on the ODISSEI Zenodo Community; other material will be shared via GitHub
All course sessions will take place in the Mandeville Building on the Erasmus Campus Woudestein, Burgemeester Oudlaan 50, Rotterdam. You can find information here and download the campus map here.
Throughout the summer school, ODISSEI will organize various social events. Read more about those in the timetable below
Each day will have three breaks. A coffee break in the morning, lunch at 12:00, and another coffee break in the afternoon.

Session 1 (10:00-12:00): Introducing ODISSEI and Computational Social Science. Speaker: Tom Emery
Session 2 (13:00-14:00): Guest lecture by Jessica Nissen, University of Turku
Session 3 (14:00-15:00): Introduction to PreFer. Speaker: Elizaveta Sivak
Session 4 (15:00-16:00): Submitting for Benchmarking. Speakers Elizaveta Sivak & Adriënne Mendrik
Session 5 (16:00-17:00): Introduction to LISS panel by Joris Mulder
Programme starts at 10:00, rather than 9:00
Location: Room: 2-22, Polak building
Social Event (17:30-20:00): Opening Drinks & Dinner @ Erasmus Paviljoen, Rotterdam

09:00-10:00: Welcome and Introduction to Machine Learning
10:10-11:00: Predictive Modeling Pipeline: Data Exploration
11:10-12:00: Predictive Modeling Pipeline: Fitting a scikit-learn Model on Numerical Data
12:00-13:00: Lunch Break
13:00-14:00: Predictive Modeling Pipeline: Fitting a scikit-learn Model on Numerical Data
14:00-15:30: Predictive Modeling Pipeline: Handling Categorical Data
15:30-16:00: Introduction to LISS Data
16:00-17:00: Guest lecture by Bruno Arpino, University of Padua
Location: Room: 3-09, Polak building

09:00-16:00: Causal Impact Analysis & Network Analysis by Erik-Jan van Kesteren, Javier Garcia-Bernardo
16:00-17:00: Guest lecture by Eva Beaujoan, online
Location: Room: 3-09, Polak building
Social Event (17:30-20:00): Drinks & Dinner at De Gele Kanarie in Rotterdam

In the second week, the teams will be given access to the CBS remote access environment and will be able to work on the same prediction task but based on the CBS administrative micro-data. They will have one week to work in their teams to develop their prediction models with the winners announced on the final Friday of the summer school. For this final element of the challenge, teams will not only be assessed by how well their model predicts the outcomes for the LISS respondents, but also the degree to which it predicts fertility behavior in the wider population (i.e. its generalizability).

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