Anomaly Detection in Python

Starting at USD 39.00
4 hours duration

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

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.

About Provider

Online Education Provider ยท 410 courses
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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