Consolidate learning and connect to broader unit goals and real-world applications
You've completed your first data quality investigation. Sarah is confident you can now handle messy real-world data with the same rigor professional analysts use.
What You Can Now Do
Statistical Tools
- • Calculate z-scores: z = (x - μ) / σ
- • Apply the ±2 rule for outlier detection
- • Compare statistics with and without outliers
- • Measure impact on planning calculations
Business Judgment
- • Distinguish errors from real events
- • Decide: keep, flag, or correct outliers
- • Document decisions with reasoning
- • Explain choices to stakeholders
Sarah can now build her forecasting models on clean, reliable data. The café's waste reduction plan depends on accurate predictions - and accurate predictions depend on clean data.
Today
Clean data + identified outliers
Next Lesson
Use clean data to predict future sales
Unit Goal
Reduce waste from 8-12% to 3%
Any Data Analysis Job
Every dataset has outliers. Banks flag suspicious transactions, healthcare identifies unusual test results, retailers spot异常的 purchase patterns.
Business Consulting
When you present numbers to clients, they will ask "why is this different from the raw data?" You need documentation and reasoning for every decision.
Research & Science
Every scientific study begins with data cleaning. Your outlier detection skills are foundational to any research career.
Quality Control
Manufacturing uses the same ±2 rule to identify defective products. The same statistical thinking applies everywhere.
With clean data in hand, Sarah can now build forecasting models to predict future sales. In the next lesson, you'll learn how to use the patterns in past data to predict what might happen next - the foundation of every business plan.