People Innovation Excellence

Data Analysis for Decision Making

Learning Outcomes:

  1. Define fundamental concepts, terms, and principles related to data analysis for decision making.
  2. Describe data patterns, relationships, analytical methods, and results using appropriate statistical and visual approaches.
  3. Analyze data, analytical outputs, and model results to generate insights that support decision making.

Topics:

  1. Describing the Distribution of a Single Variable
  2. Finding Relationships among Variables
  3. Data Exploration and Visualization
  4. Normal, Binomial, Poisson, and Exponential Distribution
  5. Decision Making under Uncertainty, MCDA, and Sensitivity Analysis
  6. Regression Analysis: Estimating Relationships
  7. Regression Analysis: Statistical Inference
  8. Logistic Regression
  9. Naïve Bayes
  10. Classification Trees

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