1. Anomaly Detection – Elldy Academy

What is data analytics?

Data cleaning? Missing Values Duplicate Data Case Sensitivity Data Types Outliers

Quick Stats Basic Stats What Is an Outlier? How to Practice Basic Stats & Outliers

1. Variance 2. Standard Deviation (SD) 3. Coefficient of Variation (CV) 4 Practice Variance, Standard Deviation (SD), Coefficient of Variation (CV)

1. Skewness 2. Kurtosis

Correlation Why We Use Correlation? Pearson Correlation (r) Spearman Correlation (ρ or rₛ)

Trend Analysis 1. Time forecasting 2. Trend Break Detection 3 Moving Average

Grouping 1. Group By Sum 2. Group By Mean (Average) 3. Group By Count 4. Group By Minimum 5. Group By Maximum 6. Group By Median

AI Insights 1. Anomaly Detection 2 .Forecast Suggestion (Predictive Forecasting) 3 Correlation Warning 4 Trend Direction Prediction 5 Seasonality Detection 6 Top Driver / Influencer Analysis 7 Productivity Improvement Prediction 8 Business Risk Warnings

CERTIFICATION

chapter 9 :AI insights

1. Anomaly Detection

Anomaly detection identifies unusual or abnormal patterns in data that deviate from expected behavior.

Example:
A retail store normally sells 100–120 units per day, but suddenly sales drop to 40 units.
→ The system flags this as an anomaly.

Analysis Process:

Collect historical sales data.
Calculate normal range or pattern.
Compare new data with expected values.
If deviation exceeds threshold → flag anomaly.
Generate alert or insight.

⬅ Previous Next ➜