Course description
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
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Training content
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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Datacamp
DataCamp offers a comprehensive platform for learning data skills, specializing in training individuals and teams in data science, analytics, and AI. With a focus on interactive, hands-on learning, DataCamp provides courses across key programming languages such as Python, R, SQL,...
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