SQL Anomaly Detection: Tuning Parameters for Backtesting

Identifying outliers in data is critical, but static SQL queries often miss the mark. To build reliable detection systems, you must learn how to test and adjust your detection parameters against historical data. This text-only course guides you through the foundational concepts of anomaly detection backtesting using SQL. You will transition from writing basic queries to systematically evaluating how lookback windows and threshold values affect your detection accuracy. What you will learn: 1. Understand the fundamentals of anomaly detection and why backtesting is essential. 2. Configure lookback periods in SQL using window functions to establish accurate baseline metrics. 3. Adjust detection thresholds to balance sensitivity and minimize false positives. 4. Analyze historical data patterns to evaluate how parameter changes impact detection quality. 5. Write clean, modular SQL queries using Common Table Expressions (CTEs) for structured backtesting. You will start with core definitions and theoretical concepts before writing SQL queries to simulate historical runs, compare parameter variations, and interpret the results. This course is designed for beginner data analysts, database administrators, and SQL developers who want to implement reliable data quality checks. Basic SQL familiarity is helpful, but no advanced statistical background is required. Start tuning your SQL queries to catch data anomalies with confidence.