Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Table of Contents: Chapter 1: Getting Started with Analytics Chapter 2: Approaches for Analytics and Data Science Chapter 3: Accessing Data from Network Components Chapter 4: Mental Models and Cognitive Bias Chapter 5: Innovative Thinking Techniques Chapter 6: Analytics Use Cases and the Intuition Behind Them Chapter 7: Analytics Algorithms and the Intuition Behind Them Chapter 8: Building Analytics Use Cases Chapter 9: Developing Real Use Cases: The Power of Statistics Chapter 10: Developing Real Use Cases: Network Infrastructure Analytics Chapter 11: Developing Real Use Cases: Control Plane Analytics Using Syslog Telemetry Chapter 12: Developing Real Use Cases: Data Plane Analytics Chapter 13: Cisco Analytics Chapter 14: Book Summary
About the Author
John Garrett is CCIE Emeritus (6204) and Splunk Certified. He earned an M.S. in predictive analytics from Northwestern University, and has a patent pending related to analysis of network devices with data science techniques.
