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Exploratory Data Analysis (EDA) is the critical first step in data science projects, systematically examining datasets to understand their characteristics, identify patterns, and assess data quality before formal modeling.

This skill turns raw data and questions into validated, documented models ready for production:

Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.

Statistical modeling, experimentation, causal inference, and production ML systems for data-driven decision-making. \n \n Covers experiment design, A/B testing, feature engineering, model evaluation, and causal analysis with Python, R, SQL, and Scala \n Includes production patterns for scalable data processing, ML model deployment, and real-time inference with monitoring and drift detection \n Supports MLOps best practices: automated retraining, feature stores, model serving, canary deployments,

Clear, effective data visualizations with chart selection rules, design principles, and storytelling techniques. \n \n Covers 10+ chart types with decision rules for when to use each (line for time series, bar for comparison, scatter for correlation, heatmap for patterns) \n Design guidelines for axes, color theory, typography, and annotations including colorblind-safe palettes and a strong stance against pie charts \n Includes ready-to-run Python/matplotlib recipes for line charts, bar charts,