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Phase 5 · Exploratory Data Analysis & Feature Engineering

Topics

EDA Process & Techniques

Part of the Data Science Roadmap.

Summary

A structured first look at any new dataset — shape, types, distributions, missing values and early patterns — done before any modeling, since garbage understanding leads to garbage models.

How to Learn This

  • 1Run a basic profile (shape, dtypes, null counts, summary stats) on any new dataset before modeling.
  • 2Build a personal checklist of the first 5 things you always check.
  • 3Practice on a genuinely messy public dataset to build pattern recognition.

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SQL Case Study for a Data Science Problem

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Handling Missing Data

More topics in Exploratory Data Analysis & Feature Engineering

Handling Missing DataOutlier Detection & TreatmentFeature Scaling (Normalization / Standardization)Encoding Categorical VariablesFeature Creation & SelectionDimensionality Reduction (PCA Overview)Handling Imbalanced DataCorrelation Analysis
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