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Exploratory Data Analysis (EDA)
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  • 👋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
    • 3. Dataset Selection and Understanding
    • 4. Data Cleaning and Preprocessing
    • 5. Techniques and Approaches
    • 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 Library
    • ⛏️Python tools
    • ®️® ® ® The R Project
    • 🌀Data Exploration
    • 🎯Data Quality
    • 📔Data Profiling
    • 📺Visualization
  • Tech Exploration
    • 🎬Youtube
    • ☁️Github
    • 🔬Lab
    • 💼Case Study
  • Reference
    • API Reference
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On this page
  1. Course Content
  2. 1. Introduction

Code & Practice

  • simplilearn: What is Exploratory Data Analysis? Steps and Market Analysis

  • Exploratory Data Analysis (EDA): Types, Tools, Process

  • projectpro: Exploratory Data Analysis in Python-Stop, Drop and Explore

  • medium.com: 10 Things to do when conducting your Exploratory Data Analysis (EDA)

  • towardsdatascience.com: An Extensive Step by Step Guide to Exploratory Data Analysis

  • EDA - Exploratory Data Analysis: Using Python Functions

  • Step-by-Step Exploratory Data Analysis (EDA) using Python

Visitors

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Last updated 2 years ago