Introduction to EDA & Understanding Public vs Private Data
Today marks a new chapter in my journey — I’ve started diving into Data Toolkits 🧰.
The first step in data analysis is EDA (Exploratory Data Analysis), where we explore datasets to uncover patterns, spot anomalies, and test assumptions.
🔹 What is EDA?
EDA is the process of summarizing the main characteristics of data using:
• Descriptive Statistics (mean, median, variance)
• Visualization (histograms, scatter plots, heatmaps)
• Data Cleaning (handling missing values, outliers)
👉 It helps analysts decide what questions to ask next.
🔹 Public vs Private Data in Analysis
📂 Public Data
• Freely available (e.g., Kaggle, UCI Machine Learning Repository, government portals).
• Great for learning, practice, and research.
🔒 Private Data
• Owned by companies/organizations (customer data, sales, financial records).
• Used for internal decision-making.
• Requires privacy laws compliance (GDPR, HIPAA, etc.).
⚡ Fun Facts
• 80% of a data analyst’s time is often spent in cleaning & exploring data, not modeling.
• The famous Titanic dataset (survival prediction) is one of the most used EDA practice datasets ever.
• Public datasets fuel competitions (like Kaggle), while private datasets drive business insights.
✨ Reflection
EDA feels like detective work 🕵️♀️ — searching for hidden clues in the data.
Excited to start applying Pandas, NumPy, Matplotlib, and Seaborn together for real analysis!
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