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

8Experimentation and Causal Inference

A/B test design fundamentals Randomization and stratification Metric selection and lift Sequential testing controls CUPED and variance reduction Quasi-experimental designs Difference-in-differences Synthetic control methods Propensity score techniques Uplift modeling Interference and spillover Sample ratio mismatch Stopping rules and peeking Ethics of experimentation Experiment platforms and tooling

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/Experimentation and Causal Inference

Experimentation and Causal Inference

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Design credible tests and causal analyses to estimate true business impact.

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Quasi-experimental designs

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