ECOD Algorithm you own this product

prerequisites
define functions and classes • use libraries • read documentation
skills learned
ECOD method
Sergio Solórzano
1 week · 8-10 hours per week · BEGINNER
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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.

project author

Sergio Solorzano

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

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