Processing Complex Data

Lecturer: Javier Garcia-Bernardo, Assistant Professor of Social Data Science, Department of Methodology & Statistics, Utrecht University.

About the course

Contrary to what most introductory data science and statistics courses teach, real-world scientific data come in an enormous variety of formats, sizes, structures, and procedures — from simple tables to spatiotemporal arrays, normalized relational schemas, nested API responses, raw scraped web pages, networks, and domain-specific scientific standards. This course gives students hands-on experience with handling, processing, and modelling six families of complex data, in a hackathon-style format where each group goes deep on one data type and teaches the rest of the class.

The narrative spine of the course is from raw traces to defensible claims. Each group works through a single pipeline: raw source → operationalized clean object → baseline model with one sensitivity check → presentation.

Course materials

• Course manual
• Project guidelines

Lectures

Week	Title	Lecture
1	What Makes Data Complex?	TBD
2	From Complex Data to Clean Data	TBD
3	Scaling Up Modeling	TBD
4	Communicating Research	TBD
5	Presentations	—
6	Short Report	—

Group projects

Variant	Data family	Example research question
Geospatial	projects/geospatial.md	What is the relation between municipal land use and population composition?
Networks	projects/networks.md	What is the relationship between gender and cross-program relations in high school?
Messy web text	projects/messy_web_text.md	Do company sustainability pages differ linguistically from public-interest climate information pages?
Relational database	projects/relational_database.md	Which driver, constructor, grid, circuit, and season characteristics are associated with F1 finishing points?
Time series	projects/time_series.md	How does an fMRI signal change across NSD scan sessions?
API data	projects/api_data.md	Which study attributes are associated with completed versus ongoing clinical trials?