Data Analytics for IT Networks

This book equips IT and network professionals with the data science skills needed to optimize network performance and security. Data Analytics for IT Networks provides practical methodologies and case studies.

In Data Analytics for IT Networks, professionals in network and IT fields are guided on how to harness the vast amounts of data collected from network infrastructures. This data holds the key to solving existing problems, enhancing security, and optimizing network performance. However, to unlock these opportunities, IT networking experts must possess a solid understanding of data science, while data scientists need to be familiar with modern networking concepts. This book serves as a bridge between these two disciplines, enabling both to extract significant value from various data sources, including telemetry and event analytics.

John Garrett, drawing from his extensive experience with large-scale Cisco networks, presents essential data science methodologies and algorithms tailored for network and IT professionals. He also provides insights for data scientists to grasp contemporary network technologies and their respective data sources. By establishing a common ground, Garrett empowers readers to identify innovative use cases that integrate data science with network data, enhancing their problem-solving capabilities.

The book culminates in practical, Python-based case studies that reflect the real-world challenges faced by Cisco Customer Experience (CX) engineers. These case studies serve as templates for developing customized solutions, ranging from advanced troubleshooting to service assurance. Readers will learn to navigate the data analytics landscape, explore network data sources, and master proven algorithms and methodologies, ultimately leading to improved network infrastructure analysis and innovation.