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

Data profiling and sanity checks Univariate analysis Bivariate and multivariate relationships Segmentation and clustering basics Outliers and anomaly cues Feature engineering ideation Visualization best practices SQL for EDA Pandas workflows Feature selection intuition Correlation vs. causation cues Sampling strategies EDA for time series EDA for categorical data Storyboarding findings

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/Exploratory Data Analysis and Business Understanding

Exploratory Data Analysis and Business Understanding

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Use EDA to uncover patterns, validate assumptions, and generate business hypotheses.

Content

11 of 15

Correlation vs. causation cues

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