Number of Videos: 3 hours – 26 lessons
Author:Arun Kejariwal
User Level: Beginner
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 processesArun 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.”
Mac Minimum System Requirements:Mac Recommended System Requirements:Processor:   AnyRAM:   AnyHard Disk:   2GBVideo Card:   AnySupported OS:   Mac El Capitan 10.11, Mac Yosemite 10.10, Mac Mavericks 10.9, Mac Mountain Lion 10.8, Mac Lion 10.7, Mac Snow Leopard 10.6, Mac Leopard 10.5, Mac OS X, Macintosh

Product Features

• Learn Understanding Anomaly Detection from a professional trainer on your own time at your own desk.
• This visual training method offers users increased retention and accelerated learning.
• Breaks even the most complex applications down into simplistic steps