Univariate Anomaly Detection with Azure: Historical and Real-Time Data

Identifying unexpected spikes or drops in your metrics is crucial for maintaining system health, detecting fraud, and understanding business trends. This text-based course introduces you to the fundamentals of univariate anomaly detection using cloud-hosted decision services. You will learn how to prepare single-feature time-series data and configure detection models to analyze both historical datasets and incoming real-time data streams. By studying clear written explanations and structured code examples, you will gain the skills to integrate automated anomaly detection into your data pipelines. What you'll learn: - Understand the fundamental concepts of univariate time-series data and anomaly detection. - Configure Azure anomaly detection services to process historical data batches. - Apply real-time detection models to identify outliers in the latest data points. - Prepare and clean single-feature datasets to ensure accurate detection results. - Integrate anomaly detection APIs into modern data workflows and observability pipelines. - Tune sensitivity settings to balance false positives and missed anomalies. The course begins with foundational definitions of time-series patterns and anomaly types, then moves step-by-step through setting up cloud services, processing historical batches, and handling streaming data. This program is designed for beginner data analysts, cloud practitioners, and developers looking to add anomaly detection to their toolkit, with no prior machine learning experience required. Start reading today to master automated anomaly detection in your data workflows.