Introduction to Anomaly Detection in R

Yes, you will receive a certificate of completion when you have finished the course.

This course would be beneficial for data scientists, fraud investigators, cybersecurity experts, and anyone who works with data that includes anomalies or suspicious records.

In this course, you'll explore statistical tests for identifying outliers, and learn to use sophisticated anomaly scoring algorithms like the local outlier factor and isolation forest.

You'll apply anomaly detection algorithms to identify unusual wines in the UCI Wine quality dataset and also to detect cases of thyroid disease from abnormal hormone measurements.

You'll use numerical and graphical summaries to informally assess whether data contain unusual points. You'll also use a statistical procedure called Grubbs' test to check whether a point is an outlier.

Local anomalies are outliers relative to their neighbors, while global anomalies are outliers relative to all other points in the dataset. You'll learn how the local outlier factor and isolation forest algorithms can help in each case.

In the final chapter, you'll learn to calculate and interpret the precision and recall statistics for an anomaly score, and how to adapt the algorithms so they can accommodate data with categorical features.