Anomaly detection: Techniques and best practices
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance. In this talk, we will introduce anomaly detection and discuss the various analytical and machine learning techniques used in in this field. Through a case study, we will discuss how anomaly detection techniques could be applied to energy data sets. We will also demonstrate, using R and Apache Spark, an application to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
Sri is the founder of www.QuantUniversity.com, a data and Quantitative Analysis Company and the creator of the Analytics Certificate program (www.analyticscertificate.com ). Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri has worked at Citigroup, Endeca, Mathworks and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics and big data in the industry and at Babson College, Northeastern University and Hult International Business School. In 2016, QuantUniversity will be offering the Analytics Certificate Program in Boston to train the next generation of analysts enabling them to leverage data science and big data technologies to scale up analytics in the enterprise.
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