Course Outline

Introduction

Anomaly Detection

  • Types of anomalies
  • Causes of anomalies
  • Zscore, Dbscan, and isolation forest

Anomaly Detection Algorithms

  • Univariate space
  • Low-dimensional space
  • High-dimensional space

Preparing the Development Environment

  • Installing and configuring SAS

Univariate Space

  • Working with algorithms
  • Masking and swamping effects

Low-Dimensional Space

  • Working with algorithms

High-Dimensional Space

  • Working with algorithms

Summary and Conclusion

Requirements

  • An understanding of SAS
  • Experience with R
  • Python experience

Audience

  • Data Scientists
  • Data Analysts
  14 Hours
 

Testimonials

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