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Exploratory Data Analysis (EDA)
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  • 👋Welcome!
  • Course Content
    • 1. Introductionchevron-right
      • 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. Fundamentalschevron-right
    • 3. Dataset Selection and Understandingchevron-right
    • 4. Data Cleaning and Preprocessing
    • 5. Techniques and Approacheschevron-right
    • 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
    • ℹ️Kaggle
  • Tools and Software
    • ✨Data Analysis Tools
    • 🐍Python Librarychevron-right
    • ⛏️Python tools
    • ®️® ® ® The R Project
    • 🌀Data Exploration
    • 🎯Data Quality
    • 📔Data Profiling
    • 📺Visualization
  • Tech Exploration
    • 🎬Youtube
    • ☁️Github
    • 🔬Lab
    • 💼Case Study
  • Reference
    • API Referencechevron-right
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  1. Course Content

1. Introduction

EDA: Uncovering Insights and Patternschevron-rightWhy EDA?chevron-rightImportance of EDAchevron-rightThe role of EDA in the data analysis processchevron-rightA Comprehensive Examinationchevron-rightCode & Practicechevron-rightBasic Conceptchevron-right
PreviousWelcome!chevron-leftNextEDA: Uncovering Insights and Patternschevron-right

Last updated 2 years ago