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LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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Price: $147

Regular price $210, discounted 10%
  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Pricing: $147

Price: $95 with Annual Ai+ Premium Subscription

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  • Access to all on-demand Ai+ Sessions (more than 100)
  • Previous ODSC Conference recordings
  • $500 credit for upcoming ODSC Conferences
  • Machine Learning Certification
  • Deep Learning Bootcamp
  • Anomaly Detection live training and recordings

Price: $147 (includes 30% discount)

  • Purchase your Anomaly Detection ticket by 2023/01/15 to redeem 30% discount


  • Anomaly Detection – 4 hours immersive session
  • Hands-on training with QA
  • Certification of Completion 



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Meet Your Instructor

Aric LaBarr

A Teaching Associate Professor in 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.

Course Overview

What’s the plan? 

The Association of Fraud Examiners (ACFE) consistently estimates that organizations lose approximately 5% of their revenues due to fraud. Based on world GDP estimates, this would be anywhere from $3-4 trillion annually. Fraud is one of the most interesting problems to try and solve because the people in your data are not trying to be found. Data science techniques are now at the forefront of this industry to help fight the battle against criminals. 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 every 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

  • 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 machines learning approaches like isolation forests and classifier adjusted density estimation (CADE)

  • Visualize these anomalies identified by the above approaches

Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

Course Overview

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Course Outline

Module 1:
Introduction to Fraud

Module 2:
Data Preparation

Module 3:
Anomaly Models

  • The Problem of Fraud – How can we analytically define fraud? There are important characteristics of fraud that puts a better perspective on the modeling and identification of fraud
  • Detection and Prevention – The two biggest pieces that any holistic fraud solution should have are detection of previous instances of fraud and prevention of new instances. This section also defines the typical fraud identification process in organizations.
  • Analytical Solution – Now that we now what fraud is as well as the organizational structure of how to deal with fraud, we need to introduce the analytical approaches to becoming a mature organization on detecting and preventing fraud.
  • Feature Engineering – The best way to glean information from data is to develop good features to help detect and identify fraud. We talk about and develop strategies for developing good features for anomaly detection.
  • RFM Features – Thinking about new features in terms of recency, frequency, and monetary impact help define important characteristics of fraud. This is where the session gets interactive as participants put on their “fraudster hat” and try to think like a criminal to help develop new features.
  • Categorical Feature Engineering – This section will cover ways to use categorical pieces of information to create even more rich features for our anomaly detection.
  • Non-statistical Techniques – This section covers Benford’s Law and why it was used (and still is) for basic anomaly detection.
  • Univariate Analysis – When addressing anomalies for one variable at a time, we can use a variety of techniques. This section covers z-scores, robust z-scores, the IQR Rule, and the adjusted IQR rule.
  • Multivariate Analysis – This is where the biggest improvements in anomaly detection have happened over the past decade. We will start with more statistical approaches like Mahalanobis distances (and their robust counterparts) as well as k-Nearest Neighbors (k-NN) and the Local Outlier Factor (LOF). Then we will move into more advanced machine learning approaches to anomaly detection like isolation forests and classifier-adjusted density estimation (CADE).
  • Wrap-up – Here will will summarize everything we have done to build up our anomaly detection as well as hint towards the next course in more advanced fraud detection models. 

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  • Introductory R/Python
  • Basic introduction to decision trees (this isn’t required, but helpful for understanding)
  • Basic introduction to classification models like logistic regression, decision trees, etc. (this isn’t required, but helpful for understanding)

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