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Anomaly detection plays a key role in today’s world of data-driven decision making. This stems from the outsized role anomalies can play in potentially skewing the analysis of data and the subsequent decision making process. This course is an overview of anomaly detection’s history, applications, and state-of-the-art techniques.

Taught by anomaly detection expert Arun Kejariwal, the course provides those new to anomaly detection with the understanding necessary to choose the anomaly detection techniques most suited to their own application. While not required, a basic understanding of statistics, R, and Python will be helpful to get the most out of the class.

Survey the history of anomaly detection in astronomy, statistics, and manufacturing
Gain a core understanding of the most important anomaly detection techniques available today
Explore the landscape of applications where anomaly detection is routinely used
Develop an awareness of the underlying assumptions and challenges of anomaly detection
Learn how to mitigate the influence of anomalies during data-driven decision making processes
Arun Kejariwal is a Statistical Learning Principal at Palo Alto based Machine Zone, where he leads R&D teams working on novel techniques for fraud detection and real-time anomaly detection. He developed many open sourced techniques for anomaly detection and breakout detection while working for Twitter; he speaks frequently at the Velocity and Strata Data conferences, and he’s the co-author of the O’Reilly title “The Art of Capacity Planning: Scaling Web Resources.”
Courses :
Introduction
Historical Overview
Applications
Techniques
Going Forward
Conclusion