How can networks help us understand and predict social systems? How to find important individuals and communities? How to predict unobserved connections between genes? How to learn the dependencies between interrelated entities? How can we stop disease or information spreading in networks? In this course, we provide participants with the conceptual and practical skills necessary to use network science tools to answer social, economic and biological questions.
This course introduces concepts and tools in network science. The objective of the course is that participants acquire hands-on knowledge on how to analyze different types of networks. Participants will be able to understand when a network approach is useful, understand different types of networks, understand the differences and similarities between a Complex Networks and a Social Network Analysis approach, describe network characteristics, infer edges or node attributes, and explore dynamical processes in networks.
The course has a hands-on focus, with lectures accompanied by programming practicals (in Python and R) to apply the knowledge on real networks, drawn from examples in sociology, economics and biology.
- Day 1: Introduction to network science and network description
- Day 2: Network formation models and statistical approaches to network analysis
- Day 3: Community detection and link prediction
- Day 4: Network Inference
- Day 5: Simple and Complex Contagion in Networks
Participants should be proficient in spoken and written English. Participants should feel comfortable programming in either Python or R (we will be using both in the course), and have a basic understanding of algebra, probability and statistics.
Teaching methods/learning formats
Each day is split into a morning and an afternoon session. In each session we first introduce a method with a focus on conceptual understanding and possible applications. This is followed by a practical in which the participants apply the method learned using real data from socioeconomic or biological settings.
Participants are requested to bring their own laptop computer. Software will be available online.
This course can be taken separately, but is also part of a series of 7 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics:
- Data Science: Programming with Python (Course code S17, 3-7 July 2023)
- Data Science: Statistical Programming with R (Course code S24, 3-7 July 2023)
- Data Science: Multiple Imputation in Practice (Course code S28, will not be available in 2023)
- Data Science: Data Analysis (Course code S31, 10-14 July 2023)
- Data Science: Network Science (this course)
- Data Science: Introduction to Text Mining with R (Course code S41, 10-13 July 2023)
- Data Science: Applied Text Mining (Course code S42, 17-21 July 2023)
Upon completing, within 5 years, 3 out of 7 courses in the Summer School Data Science specialisation (no more than one text mining course), students can obtain a certificate.
Please see here for more information about the full specialisation.
Participants with some technical background eager to learn about network science.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
This course is suitable for the STAP budget; if you are accepted to this course, you can request the correct STAP form via firstname.lastname@example.org.
Utrecht Summer School does not offer scholarships for this course.
We also offer tailormade M&S courses and in-house M&S training. If you want to look the possibilities, please contact us at email@example.com
Team M&S Summer School | E: firstname.lastname@example.org