Anomaly Detection

Develop Z-score and Baseline Results you own this product

This project is part of the liveProject series Three Anomaly Detection Methods
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

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Look inside

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.

book resources

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

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

you will learn

In this liveProject, you’ll learn to build, implement, and evaluate anomaly detection algorithms.

  • Evaluate anomaly detection algorithms using ROC curve, ROC-AUC, precision recall, average precision, and anomaly injection
  • Build a z-score anomaly detection algorithm
  • Become familiar with classification error categories

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.

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  • five seats for your team
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  • Develop Z-score and Baseline Results project for free