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WGU VPC2 Data-Driven Decision Making C207 Exam Syllabus

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Before starting your VPC2 Data-Driven Decision Making C207 exam preparation, it is recommended to review the complete WGU VPC2 Data-Driven Decision Making C207 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 VPC2 Data-Driven Decision Making C207 questions. We also provide premium VPC2 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
VPC2 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 VPC2 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