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WGU Data-Driven Decision Making (C207) Exam Syllabus

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Before starting your Data-Driven Decision Making (C207) exam preparation, it is recommended to review the complete WGU Data-Driven Decision Making exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free Data-Driven Decision Making (C207) questions. We also provide premium Data-Driven Decision Making (C207) practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

WGU
Vendor
Data-Driven Decision Making (C207)
Exam Code
123
Total Questions
6
Total Exam Domains

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Data-Driven-Decision-Making EXAM QUESTIONS

WGU Data-Driven Decision Making (C207) Exam Objectives

Section 1: The Case for Quantitative Analysis
Weight:
-
  • Definition and scope of analytics vs. quantitative analysis
  • Types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
  • Davenport and Kim's Three Stage Model of quantitative decision making (framing the problem, solving the problem, communicating results) 
  • Scales of measurement: Nominal, Ordinal, Interval, and Ratio
  • Discrete vs. continuous data
  • Data errors: Random, Systematic, Omission, and Outlier errors
  • Business intelligence and the role of data in organizational strategy
  • Results-Based Management (RBM) stages: Inputs, Activities, Outputs, Outcomes
Section 2: Statistics as a Managerial Tool
Weight:
-
  • Hypothesis testing: Null hypothesis vs. Alternative hypothesis
  • p-values and statistical significance
  • t-tests: One-sample t-test and Two-sample t-test
  • Analysis of Variance (ANOVA)
  • Decision tree analysis: constructing and interpreting trees, expected values
  • Probability concepts and distributions
  • Normal distribution and Standard Normal Distribution (Z-scores)
  • Scatter plots, histograms, and data visualization tools
  • Linear regression analysis: conducting regression in Excel, creating scatterplots with trendlines, and interpreting R-squared values
  • Correlation vs. causation
Section 3: More Statistical Tools
Weight:
-
  • Confidence intervals
  • Chi-square tests
  • Sampling methods and sampling bias
  • Experimental design: blinding, double-blinding, control groups
  • Time-series analysis and forecasting
  • Sensitivity analysis
  • Break-even analysis
Section 4: Quality Metrics and Tools
Weight:
-
  • Quality assurance vs. quality control
  • Statistical Process Control (SPC) and control charts
  • Six Sigma concepts (DMAIC methodology)
  • Total Quality Management (TQM) principles
  • Cause-and-effect (Ishikawa/Fishbone) diagrams
  • Pareto charts and the 80/20 rule
  • Process capability analysis
  • Benchmarking and performance standards
  • Key Performance Indicators (KPIs) and dashboards
Section 5: Real World Data-Driven Decisions
Weight:
-
  • Business intelligence (BI) systems and tools
  • Knowledge management systems
  • Big Data characteristics (Volume, Velocity, Variety)
  • Data visualization best practices
  • Balanced Scorecard and enterprise performance frameworks
  • Data governance and data integrity
  • Interpreting decision tree analysis and linear regression in real-world business contexts 
  • Ethical use of data and privacy considerations
Section 6: Improving Organizational Performance
Weight:
-
  • Performance management frameworks
  • Continuous improvement methodologies (Kaizen, Lean)
  • Criterion-referenced assessment and competency benchmarking
  • Balanced Scorecard perspectives (Financial, Customer, Internal Process, Learning & Growth)
  • Using analytics to drive strategic decisions
  • Workforce analytics and HR data metrics
  • Criterion-referenced testing and competency-based evaluation at WGU