Skip to main content
HomePython

course

Anomaly Detection in Python

Intermediate
Updated 12/2024
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Start course for free

Included for FreePremium or Teams

PythonProbability & Statistics4 hours16 videos59 exercises4,950 XP4,444Statement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

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.

Prerequisites

Supervised Learning with scikit-learn
1

Detecting Univariate Outliers

Start Chapter
2

Isolation Forests with PyOD

Start Chapter
3

Distance and Density-based Algorithms

Start Chapter
4

Time Series Anomaly Detection and Outlier Ensembles

Start Chapter
Anomaly Detection in Python
Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

Enroll now

Join over 15 million learners and start Anomaly Detection in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.