107 results for "topology"

Topology refers to network topology, which describes the actual logical and physical layout of the network, from the way that the network nodes are in place, to how they connect with the other nodes. Topology is the organized mapping and communications structure for a network of computers that exchange data among each other.

There are two types of network topologies. In these two logical and physical topologies in computers, the logical topology concentrates on data transfer patterns between nodes in a network, while the physical topology focuses on how the nodes and connected devices are actually laid out.‎

Learn about topology because it's an important characteristic of computer networks. It's in a visual format, which makes it easier to learn. Topology also has aspects that can be applied to statistics, data science, chemistry, physics, and other sciences.

It has real-world applications too. When you learn about topology, the theory involved can help you set up a small home or business network on your own. That skill is becoming essential, as our world becomes increasingly more connected.‎

Some of the careers that use topology knowledge include data science managers, mathematical science teachers, mathematicians, natural sciences analysts, and other science and technology professions. You may even see elements of topology in careers in virtual reality, visual modeling, and network capabilities. There are jobs in the visual arts and film industries that encompass network understanding that topology can give you.‎

When you study topology in online courses, you'll expose yourself to understanding cloud networks, networked nodes, information architecture, visual network modeling, and other areas of interest. These online courses are taught by experts in their field, with years of experience in the history of how networks have evolved over time. As you grow your career using concepts of topology, you'll realize the importance of this knowledge in statistical approaches, network formations, and machine learning.‎