Anomaly Detection

ECOD Algorithm you own this product

This project is part of the liveProject series Three Anomaly Detection Methods
prerequisites
define functions and classes • use libraries • read documentation
skills learned
ECOD method

pro $24.99 per month

  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • share your subscription with another person
  • choose one free eBook per month to keep
  • exclusive 50% discount on all purchases

lite $19.99 per month

  • access to all Manning books, including MEAPs!

team

5, 10 or 20 seats+ for your team - learn more


Look inside

Sigma Corp, a large conglomerate of energy production companies, has recently implemented anomaly detection algorithms and is generally pleased with their performance. However, analysts report that not all anomalies are being identified and the algorithms are too slow at times. As a lead data scientist at Sigma, it’s up to you to address these concerns. To increase the robustness of the algorithms, you’ll implement and optimize the probability-based Empirical Cumulative distribution-based Outlier Detection (ECOD) method, an alternative to statistical methods. You’ll benchmark the ECOD method in order to compare its performance with the statistical MD and PCA methods Sigma is currently using. When you’re finished, you’ll have firsthand experience implementing the highly efficient ECOD method to detect anomalies in multidimensional data.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Sergio Solórzano

Sergio Solórzano holds a PhD in physics from ETH Zürich, where he specialized in computational physics and published various papers on numerical algorithms for physical simulation and analysis. Currently, he’s a senior researcher and developer at Exeon Analytics, developing systems for anomaly detection in cybersecurity.

prerequisites

This liveProject is for beginner data scientists who want to learn how to use the ECOD method to implement an optimized, highly efficient anomaly detection algorithm for multidimensional data. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Basic Python
  • Basic NumPy
  • Basic Matplotlib (or Seaborn or Bokeh)
  • Basic data science
TECHNIQUES
  • Basic testing

you will learn

In this liveProject, you’ll learn to implement the highly efficient ECOD method for anomaly detection in multidimensional data.

  • Measure of skewness
  • Cumulative distribution function
  • Optimize the implementation of the ECOD anomaly detection algorithm
  • Test your implementation against non-trivial multidimensional synthetic anomalies

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

choose your plan

team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • ECOD Algorithm project for free