Develop Z-score and Baseline Results you own this product

prerequisites
define functions and classes • notions of mean value and variance
skills learned
z-score method • evaluate algorithms using ROC AUC metrics • basic synthetic anomalies
Sergio Solórzano
1 week · 6-8 hours per week · BEGINNER
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Failure is not an option for Sigma Corp. As a lead data scientist for the large conglomerate of energy production companies, it’s up to you to help ensure interruption-free operations by developing a means for detecting anomalies that signal potential problems. Using metrics, including the receiver operating characteristic (ROC) curve and the area under curve (AUC) score, you’ll evaluate anomaly detection algorithms. You’ll build a z-score anomaly detection algorithm, which focuses on a single feature and provides a simple benchmark, and you’ll apply it to a dataset to establish a reference for comparison. When you’re finished, you’ll have a firm grasp of z-score anomaly detection, classification error categories, and evaluating anomaly detection algorithms.

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 interested in learning the sought-after skills of building, implementing, and evaluating anomaly detection algorithms. 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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