This accessible three-day executive education course provides an intensive introduction to the field of complexity as it relates to Networks and Big Data.
The course is designed to teach participants to better understand analyses based on network math, and there is a focus on business applications.
We are in an age of information, with nearly every scientific field awash in new data. Thus, making sense of large sets of real-world data stands as a preeminent challenge for modern science. Massive data sets, whether they record food web relationships, online friendships, or distributions of utilities like electricity, are often described by mathematical network models that give structure to the data – and help us better understand the relationships hidden within it.
As we seek out the structures, patterns and attributes of large data sets, we also pursue the broader question of how a network’s structure gives rise to its dynamics. In doing so, we hope to understand the similarities and differences between social networks, economies, power grids, and food webs.
This accessible three-day executive education course provides an intensive introduction to the field of complexity as it relates to Networks and Big Data. Through lectures, exercises, and interactive discussions with prominent SFI faculty and your fellow participants, you will learn how methods and tools at the forefront of complexity science are being applied to modeling, predicting, and impacting the behavior of systems across many disciplines.
This course is specifically designed for professionals, faculty, students, and others who are eager to explore and apply ideas from complexity in their own fields. No background in science or mathematics is required. We particularly encourage professionals, managers and policy-makers in business, government, and nonprofit organizations; industrial research and development staff; social work and education professionals; journalists; and university faculty and students to take part in this collaborative opportunity to learn, and apply, the latest approaches to critical problems.
Location: Hyatt Centric Times Square New York
Day 1: Introduction to Networks
- Overview of the short course and Introduction to networks
- Insights from basic network statistics
- Mini Project: Software platform options and downloads, discussion on tools and uses.
Day 2: The Dynamics of Networks
- Processes on networks (information flow, disease spread, etc)
- Networks that change over time
- Mini-project: build a small network model
Day 3: Next-generation Network Tools for Complex, Big Data
- Multi-layer networks
- Applications: economics and finance, social networks
- Mini-project: big data network visualizations
Short Course Director: Michelle Girvan
Michelle Girvan is an Associate Professor in the Department of Physics and the Institute for Physical Science and Technology at the University of Maryland, College Park. She is also a member of the External Faculty at the Santa Fe Institute. Her research operates at the intersection of statistical physics, nonlinear dynamics, and computer science and has applications to social, biological, and technological systems. More specifically, her work focuses on complex networks and often falls within the fields of computational biology and sociophysics. While some of the research is purely theoretical, Girvan has become increasingly involved in using empirical data to inform and validate mathematical models.