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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Welcome!

Learn everything you need to know about exploratory data analysis, a method used to analyze and summarize data sets.

Next1. Introduction

Last updated 1 year ago

About Us

The information on this e-book is part of the materials for the subject High Performance Data Processing (SECP3133). This folder contains general Exploratory Data Analysis (EDA) information as well as EDA case studies using Malaysian datasets. This case study was created by a , Universiti Teknologi Malaysia student.

๐Ÿ‘‹
Bachelor of Computer Science (Data Engineering)
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