๐Ÿ”ฅ
Exploratory Data Analysis (EDA)
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  • ๐Ÿ‘‹Welcome!
  • Course Content
    • 1. Introduction
      • EDA: Uncovering Insights and Patterns
      • Why EDA?
      • Importance of EDA
      • The role of EDA in the data analysis process
      • A Comprehensive Examination
      • Code & Practice
      • Basic Concept
    • 2. Fundamentals
      • Lifecycle
        • Data Science
        • EDA
    • 3. Dataset Selection and Understanding
      • Kaggle
      • Github
    • 4. Data Cleaning and Preprocessing
    • 5. Techniques and Approaches
      • Types of EDA
    • 6. Data Visualization
    • 7. Statistical Measures and Hypothesis Testing
    • 9. Case Studies
    • 11. Best Practices and Tips for Effective EDA
    • 12. Future Trends and Emerging Technologies
  • Dataset
    • โ„น๏ธKaggle
  • Tools and Software
    • โœจData Analysis Tools
    • ๐ŸPython Library
      • ๐ŸผPandas
      • ๐ŸงŠNumpy
      • ๐Ÿ“ŠMatplotlib
      • ๐Ÿ“ˆSeaborn
      • ๐Ÿ“ถPlotly
      • ๐ŸคนSciPy
      • ๐Ÿ’ซStatsmodels
      • ๐Ÿ‘‚Scikit-learn
      • ๐Ÿ—ณ๏ธYellowbrick
    • โ›๏ธPython tools
    • ยฎ๏ธยฎ ยฎ ยฎ The R Project
    • ๐ŸŒ€Data Exploration
    • ๐ŸŽฏData Quality
    • ๐Ÿ“”Data Profiling
    • ๐Ÿ“บVisualization
  • Tech Exploration
    • ๐ŸŽฌYoutube
    • โ˜๏ธGithub
    • ๐Ÿ”ฌLab
    • ๐Ÿ’ผCase Study
  • Reference
    • API Reference
      • Pets
      • Users
      • Quick Start
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  1. Course Content

1. Introduction

PreviousWelcome!NextEDA: Uncovering Insights and Patterns

Last updated 1 year ago

EDA: Uncovering Insights and Patterns
Why EDA?
Importance of EDA
The role of EDA in the data analysis process
A Comprehensive Examination
Code & Practice
Basic Concept