Preview of Our Next Ai+ Training Session on Anomaly Detection with Aric LaBarr
Anomalies. Oxford dictionary defines them as things that deviate from what is typical or expected. No matter what field you are in, they seem to pop up and occur without warning. In the realm of data, anomalies can lead to incorrect or out-of-date decisions. This means we need to find them before they become too much of a problem! Whether you are just cleaning your data for analysis, monitoring the health of your computer systems, looking out for cybersecurity threats, or sifting through claims and transactions looking for fraud, anomalies drastically impact any analysis to be done. This is why we need data science to help us identify and flag anomalous data points before their impact is felt too much via anomaly detection.
In Aric LaBarr, PhD’s upcoming Ai+ Training session, Anomaly Detection & Introduction to Fraud Modeling, on February 8th, you’ll learn everything you need to detect anomalies and prevent fraud.
This course outlines the typical fraud framework at an organization and where data science can play a role. It will also lay out how to build an analytically advanced fraud system at an organization. Moving beyond just simple rules and anomaly detection, these supervised and unsupervised approaches to fraud modeling will help an organization combat the ever-present problem of fraud. These fraud modeling approaches can also be used in other industries to help organizations find unique customers or problems that might exist in their current systems.
Learning objectives include:
- Develop good features (recency, frequency, and monetary value as well as categorical transformations) for detecting and preventing fraud
- Identify anomalies using statistical techniques like z-scores, robust z-scores, Mahalanobis distances, k-nearest neighbors (k-NN), and local outlier factor (LOF)
- Identify anomalies using machine learning approaches like isolation forests and classifier-adjusted density estimation (CADE)
- Visualize these anomalies identified by the above approaches
Learn more about the session with Aric LaBarr on anomaly detection and register here!
About the instructor:
A Teaching Associate Professor at the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first Master of Science in Analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.
Originally posted on OpenDataScience.com
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