WGU C207 Final Exam Study Guide: Data – Driven Decision Making | Actual
verified study complete Solutions | 2026/27 Updates | 100% correct | Passed
on First Attempt
C207 Data-Driven Decision Making
Focused Study Session (Test-Day Ready)
—
1. Analytics & Decision-Making Foundations
Types of Analytics
Know these cold — they appear early and often.
• Descriptive analytics
→ What happened?
Example: Sales reports, averages, historical dashboards.
• Predictive analytics
→ What is likely to happen?
Example: Regression, forecasting demand.
• Prescriptive analytics
→ What should we do?
Example: Optimization, decision trees, linear programming.
Key exam trigger:
If the question says optimize, minimize cost, maximize profit, or choose the best option → Prescriptive analytics
—
2. Data Types & Data Quality
Types of Data
• Nominal – Categories with no order
Example: Yes/No, Gender, Defective/Not Defective
• Ordinal – Ranked categories
Example: Gold, Silver, Bronze
• Interval – Equal spacing, no true zero
Example: Temperature (°F, °C)
• Ratio – Equal spacing + true zero
Example: Revenue, Weight, Time
Data Quality Issues
Common red flags tested:
• Missing data
, • Misspelled data (especially for nominal variables)
• Outliers (when inappropriate)
• Out-of-range values
Data quality management focuses on:
• Cleaning data
• Reducing incomplete data
• Ensuring reliable input
—
verified study complete Solutions | 2026/27 Updates | 100% correct | Passed
on First Attempt
C207 Data-Driven Decision Making
Focused Study Session (Test-Day Ready)
—
1. Analytics & Decision-Making Foundations
Types of Analytics
Know these cold — they appear early and often.
• Descriptive analytics
→ What happened?
Example: Sales reports, averages, historical dashboards.
• Predictive analytics
→ What is likely to happen?
Example: Regression, forecasting demand.
• Prescriptive analytics
→ What should we do?
Example: Optimization, decision trees, linear programming.
Key exam trigger:
If the question says optimize, minimize cost, maximize profit, or choose the best option → Prescriptive analytics
—
2. Data Types & Data Quality
Types of Data
• Nominal – Categories with no order
Example: Yes/No, Gender, Defective/Not Defective
• Ordinal – Ranked categories
Example: Gold, Silver, Bronze
• Interval – Equal spacing, no true zero
Example: Temperature (°F, °C)
• Ratio – Equal spacing + true zero
Example: Revenue, Weight, Time
Data Quality Issues
Common red flags tested:
• Missing data
, • Misspelled data (especially for nominal variables)
• Outliers (when inappropriate)
• Out-of-range values
Data quality management focuses on:
• Cleaning data
• Reducing incomplete data
• Ensuring reliable input
—
