I first learned about networks while working at the National Security Agency. Everything is a network at NSA. The power of the network beyond its superior analytical capabilities, is the mathematical underpinnings. Graph Theory is a very powerful tool used in machine learning models. While I’m not going to teach you graph theory, I am going to give you enough of a foundation to allow your base analytics to become input to machine learning solutions.
Network analysis is the exploration of relationships between objects. Networks are great models for systems. You can describe the major parts of a system with the network nodes, and then illustrate the relationships between the system parts using the edges (or connectors or lines, use what works for you).
Let’s say your team has been given the task to reduce costs in producing your products. A good first principles thinker would want to organize the products and their irreducible parts. Enter the network. The nodes become products, and the features of the nodes (metadata, characteristics, etc.) are the parts. The team can connect products that use the same parts with the edges. Boom! The team can then explore the most highly connected parts to see if they can be made cheaper (using first principles thinking, of course).
Network Analysis Training
Network analysis training also helps you become better data curators. As you start to think about the relationships between data, you can help organize data in more efficient and impactful ways. Network analysis also helps you ferret out hidden systems. Almost every network I have created has lead me to other related networks.
In the class, we’ll explore the basic features of a network. We will also learn about the difference between undirected and directed networks. And don’t worry, this class has zero math. Network analysis is a tool, and I will show you how to use this tool to organize your data to support problem solving and ideation.