Network Science Online Course

Last Updated on August 8, 2022 by Team College Learners

Network science is a broad interdisciplinary field, encompassing disciplines ranging from physics, computer science and engineering, to the social and biological sciences. Central to network theory is the study of networks that exist in the real world and consist of many interconnected elements, such as people or genes. Therefore, understanding how these networks operate is a fundamental goal for researchers. Network science provides tools for exploring the high-level organization of these complex systems as well as their microscopic dynamics. This network science course will introduce you to network science through lectures on models of networks, including random networks and small-world networks, social and information networks, network dynamics and processes on networks. You will learn about network algorithms and techniques for extracting useful information from real-world data sets.

Networks are at the center of everything in our digital world. Develop a foundational understanding of network science, including network modeling and the analysis of large datasets. This networking courses online goes beyond the surface to help you develop an intuition for how data structures, algorithms, and graph theory can be used to understand and model structures in real-world graphs and social networks. By the end of this course, you will have strong programming skills to work with networks in Python, using the latest network library and state-of-the-art algorithms from operations research and machine learning.

The Science of Networks | World Academy of Art & Science

free online computer networking courses with certificates

How do revolutions emerge without anyone expecting them? How did social norms about same sex marriage change more rapidly than anyone anticipated? Why do some social innovations take off with relative ease, while others struggle for years without spreading? More generally, what are the forces that control the process of social evolution –from the fashions that we wear, to our beliefs about religious tolerance, to our ideas about the process of scientific discovery and the best ways to manage complex research organizations? The social world is complex and full of surprises. Our experiences and intuitions about the social world as individuals are often quite different from the behaviors that we observe emerging in large societies. Even minor changes to the structure of a social network – changes that are unobservable to individuals within those networks – can lead to radical shifts in the spread of new ideas and behaviors through a population. These “invisible” mathematical properties of social networks have powerful implications for the ways that teams solve problems, the social norms that are likely to emerge, and even the very future of our society. This course condenses the last decade of cutting-edge research on these topics into six modules. Each module provides an in-depth look at a particular research puzzle -with a focus on agent-based models and network theories of social change -and provides an interactive computational model for you try out and to use for making your own explorations! Learning objectives – after this course, students will be able to… – explain how computer models are used to study challenging social problems – describe how networks are used to represent the structure of social relationships – show how individual actions can lead to unintended collective behaviors – provide concrete examples of how social networks can influence social change – discuss how diffusion processes can explain the growth social movements, changes in cultural norms, and the success of team problem solving

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest – even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students’ programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

  • Specifically designed for introductory-level undergraduate courses in disciplines that use networks, such as business, communications, cognitive science, neuroscience, sociology, biology, and engineering, among others
  • Utilizes clear language with no technical jargon and extensive use of full color figures to illustrate key concepts
  • Allows students to immediately start writing Python code to manipulate, visualize, and analyze real-world networks
  • Includes an abundance of worked examples and homework problems to reinforce students’ understanding of the material and an extensive suite of programming tutorials are hosted online to develop practical coding skills