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Data Science for Business Decision Making

Chapters

1Foundations of Data Science in Business

The data-to-decisions value chain Analytics roles and team structures Decision types: strategic, tactical, operational Analytics maturity models Reasoning under uncertainty and risk Balancing data and intuition Tooling overview: Python, SQL, BI CRISP-DM and alternative lifecycles Business use case landscape Build vs. buy considerations Data culture essentials Stakeholder mapping and influence Metrics hierarchy and cascades Privacy and compliance overview Quick-win case walkthrough

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

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/Foundations of Data Science in Business

Foundations of Data Science in Business

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Establish core concepts, roles, and the analytics lifecycle in a business context.

Content

12 of 15

Stakeholder mapping and influence

Original version

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