Data Science

EDA General Steps - Master Checklist

Arun Murali

Tags: data-science; eda; exploratory-data-analysis; workflow

🟪 1-Minute Summary

EDA (Exploratory Data Analysis) is the systematic examination of data before modeling. Standard workflow: (1) Load and understand structure, (2) Check data types and memory, (3) Identify missing values, (4) Detect duplicates, (5) Find outliers, (6) Analyze distributions (univariate), (7) Explore relationships (bivariate/multivariate), (8) Document findings. EDA informs cleaning, feature engineering, and model selection.

🟦 Core Notes (Must-Know)

The EDA Checklist

Step 1: Load and Understand Structure

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Step 2: Check Data Types and Memory

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Step 3: Identify Missing Values

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Step 4: Detect Duplicates

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Step 5: Find Outliers

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Step 6: Analyze Distributions (Univariate)

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Step 7: Explore Relationships (Bivariate/Multivariate)

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Step 8: Document Key Findings

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🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

  1. “Walk me through how you’d start exploring a new dataset”

    • [Answer: Follow the 8-step checklist]

  2. “What are the most important things to check first?”

    • [Answer: Data types, missing values, target distribution]

  3. “How do you decide which visualizations to use?”

    • [Answer framework to be filled in]

🟥 Common Mistakes (Traps to Avoid)

Mistake 1: Jumping straight to modeling

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Mistake 2: Not documenting EDA findings

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Mistake 3: Treating EDA as one-time activity

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🟩 Mini Example (Quick Application)

Scenario

[New dataset exploration example]

Solution

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df = pd.read_csv('data.csv')

# Step 1: Structure
print(df.shape)
print(df.head())
print(df.info())

# Step 2: Data types
print(df.dtypes)

# Step 3: Missing values
print(df.isnull().sum())

# Continue with remaining steps...

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