In today's world where data-driven decision making is a norm, anomaly detection plays an important role. This is due to the fact that anomalies can potentially skew the analysis of data and the subsequent decision-making process. This course provides an overview of anomaly detection's history and applications, as well as the most current techniques.
Arun Kejariwal is an anomaly detection expert. The course gives those who are new to the field the knowledge they need to select the best anomaly detection technique for their particular application. Although not necessary, you will benefit from the class by having a basic understanding in statistics, R, Python, and other programming languages.
- Explore the history of anomaly detections in astronomy, statistics and manufacturing
- Get a solid understanding of the most important techniques for detecting anomalies today
- Explore the range of applications where anomaly detection is used routinely
- Be aware of the assumptions and challenges that can lead to anomaly detection
- Learn how to reduce the impact of anomalies in data-driven decision making processes
Arun Kejariwal, a Statistical Learning Principal at Palo Alto-based Machine Zone, leads R&D teams that develop novel techniques for fraud detection. While working for Twitter, he developed many open-source techniques for anomaly detection. He is also the coauthor of the O'Reilly title "The Art of Capacity Planning. Scaling Web Resources."
