Data Science: Network Science

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.

Program:

• 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

Entry requirements:

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:

1. Data Science: Programming with Python  (Course code S17, 3-7 July 2023)
2. Data Science: Statistical Programming with R (Course code S24, 3-7 July 2023)
3. Data Science: Multiple Imputation in Practice (Course code S28, will not be available in 2023)
4. Data Science: Data Analysis (Course code S31, 10-14 July 2023)
5. Data Science: Network Science (this course)
6. Data Science: Introduction to Text Mining with R (Course code S41, 10-13 July 2023)
7. 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.