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Data Science for Business Decision Making

Chapters

1Foundations of Data Science in Business

2Business Problem Framing and Decision Science

Problem decomposition techniques Hypothesis trees and MECE thinking Objectives, constraints, and assumptions Decision trees and influence diagrams Priors and belief updating Cost of error and decision thresholds Defining KPIs and guardrails Back-of-the-envelope sizing Choosing experiment vs. model vs. heuristic Opportunity sizing and prioritization Success criteria and acceptance tests Stakeholder alignment workshops Scoping analytics projects Risk registers and mitigations Decision cadence and governance

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

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/Business Problem Framing and Decision Science

Business Problem Framing and Decision Science

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Translate ambiguous goals into solvable analytics problems with clear success criteria.

Content

7 of 15

Defining KPIs and guardrails

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