AN INTRODUCTION TO COMPUTATIONAL SOCIAL SCIENCE

The course aims at introducing students to the application of computational tools to explore significant social phenomena.
At the same time, it will illustrate applications of the blooming field of computational social science to humanities fields such as history, literary analysis, and the history of science.
Lectures will be interactive and will require students to develop in the classroom simple computational examples in Python.

The course aims at developing skills that students can use in research but also in different innovative professional fields, such as web analytics and social media analysis.


1. Knowledge and Understanding. Students are expected to gain knowledge of the fundamental concepts of Computational Social Science and understand how they explain relevant social and cultural phenomena.
2. Applied Knowledge and Understanding. Students will develop the ability to apply basic concepts to specific models and tools of data analysis, and improve their programming skills.
3. Judgment skills. Students will learn to critically compare alternative modeling strategies and will develop individual and group examples and applications.
4. Communication skills. Students will learn to communicate in groups through teamwork opportunities and the presentation of their work in class.
5. Learning skills. The course will improve students' ability to learn atta through the use of interactive multimedia tools.

The course presupposes that students have learned some basic coding tools and have acquired basic notions and tools of computational linguistics.

Part 1. Computational social science for the humanities, with applications to history and literature

- What is computational social science?
- Detecting historical trends through language big data
- Simple computational models of social phenomena. (Examples: Choice, contagion, discrimination, population dynamics).
- Social network theory, with applications to literature and history


Part 2. Machine learning
- A gentle intorduction to machine learning
- Using scikit-learn
- Applications of machine learning: classification

During the course, reading materials will be distributed by the teacher, together with teaching colab code notebooks.

The final evaluation will be based on individual projectwork during the course (1/3 of the evaluation) and on a written final exam (2/3 of the evaluation)

The course will combine frontal teaching, individual and group projectwork by students and interactive coding applications during lectures.

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Accessibility, Disability and Inclusion
Accommodation and support services for students with disabilities and students with specific learning impairments

Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support
services and accommodation available to students with disabilities. This includes students with
mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

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