Detecting anomalies in data analysis is crucial for accurate statistical exploration. Anomalies, which are extreme values, can disrupt analyses and skew the performance of machine learning models if left undetected. In this course, you will learn how to use various anomaly detection methods using Python. You will visually identify extreme values and apply statistical techniques like Median Absolute Deviation for univariate datasets. For multivariate data, you will utilize estimators such as Isolation Forest, k-Nearest-Neighbors, and Local Outlier Factor. Additionally, you will learn how to combine multiple outlier classifiers into a reliable final estimator. By mastering anomaly detection with Python, you will acquire a valuable tool for data science. Expanding your Python statistical toolkit to include anomaly detection will enhance your understanding of data, enabling better root cause analysis and effective communication regarding system behavior. This skill will prove beneficial in tasks such as data cleaning, fraud detection, and identifying system disturbances.