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Data Science for Business Decision Making
Chapters

1Foundations of Data Science in Business

2Business Problem Framing and Decision Science

3Data Strategy, Governance, and Ethics

4Data Acquisition and Quality Management

5Exploratory Data Analysis and Business Understanding

6Descriptive Analytics and BI for Operators

7Statistical Thinking for Decision Making

Probability foundationsBusiness-relevant distributionsSampling and confidence intervalsHypothesis testing and errorsPower and sample sizeBayesian thinkingRegression fundamentalsRegularization conceptsMulticollinearity and VIFHeteroskedasticity and remediesNonparametric methods overviewBootstrapping and resamplingMissing data mechanismsTime dependence pitfallsInterpreting p-values responsibly

8Experimentation and Causal Inference

9Predictive Modeling for Business Outcomes

10Time Series and Forecasting

11Customer and Marketing Analytics

12Operations, Pricing, and Optimization

13Data Storytelling, Visualization, and Influence

14Productizing Analytics: MLOps and Deployment

15Measuring Impact, ROI, and Change Management

Courses/Data Science for Business Decision Making/Statistical Thinking for Decision Making

Statistical Thinking for Decision Making

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Apply core statistical concepts to quantify uncertainty and support decisions.

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5 of 15

Power and sample size

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