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Anomaly Detection in Python

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

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4 Hours16 Videos59 Exercises
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Course Description

Spot Anomalies in Your Data Analysis


Extreme values or anomalies are present in almost any dataset, and it is critical to detect and deal with them before continuing statistical exploration. When left untouched, anomalies can easily disrupt your analyses and skew the performance of machine learning models.

Learn to Use Estimators Like Isolation Forest and Local Outlier Factor


In this course, you'll leverage Python to implement a variety of anomaly detection methods. You'll spot extreme values visually and use tested statistical techniques like Median Absolute Deviation for univariate datasets. For multivariate data, you'll learn to use estimators such as Isolation Forest, k-Nearest-Neighbors, and Local Outlier Factor. You'll also learn how to ensemble multiple outlier classifiers into a low-risk final estimator. You'll walk away with an essential data science tool in your belt: anomaly detection with Python.

Expand Your Python Statistical Toolkit


Better anomaly detection means better understanding of your data, and particularly, better root cause analysis and communication around system behavior. Adding this skill to your existing Python repertoire will help you with data cleaning, fraud detection, and identifying system disturbances.
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  1. 1

    Detecting Univariate Outliers

    Free

    This chapter covers techniques to detect outliers in 1-dimensional data using histograms, scatterplots, box plots, z-scores, and modified z-scores.

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    What are anomalies and outliers?
    50 xp
    Print a 5-number summary
    100 xp
    Histograms for outlier detection
    100 xp
    Scatterplots for outlier detection
    100 xp
    Box plots and IQR
    50 xp
    Boxplots for outlier detection
    100 xp
    Calculating outlier limits with IQR
    100 xp
    Using outlier limits for filtering
    100 xp
    Using z-scores for Anomaly Detection
    50 xp
    Finding outliers with z-scores
    100 xp
    Using modified z-scores with PyOD
    100 xp
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Collaborators

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James Chapman
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Maham Khan
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George Boorman

Prerequisites

Supervised Learning with scikit-learn
Bex Tuychiyev HeadshotBex Tuychiyev

Kaggle Master, Data Science Content Creator

Bex is a Top 10 AI writer on Medium and a Kaggle Master with over 10k followers. He loves writing detailed guides, tutorials, and notebooks on complex data science and machine learning topics with a bit of a sarcastic style.
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