Learn how to find the unusual, interesting, extreme, or inaccurate parts of your data.

From the back cover:

Outlier Detection in Python is a comprehensive guide to the statistical methods, machine learning, and deep learning approaches you can use to detect outliers in different types of data. Throughout the book, you'll find real-world examples taken from author Brett Kennedy's extensive experience developing outlier detection tools for financial auditors and social media analysis. Plus, the book's emphasis on interpretability ensures you can identify why your outliers are unusual and make informed decisions from your detection results. Each key concept and technique is illustrated with clear Python examples. All you'll need to get started is a basic understanding of statistics and the Python data ecosystem.

About the reader:

For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics.