EDA - Exploratory Data Analysis

• A Complete Tutorial which teaches Data Exploration in detail

  * steps involved to understand, clean and prepare your data for building your predictive model
      - Data Exploration: understand and clean data
          - Variable Identification
          - Univariate
          - Bivariate Analysis
          - Missing Values Imputation 
          - Outliers Treatment
      - Feature Engineering: extracting more information from existing data
          - Variable transformation
          - Variable / Feature creation
  

1. Steps of Data Exploration and Preparation

• Data Exploration: understand and clean your data

  * [1. Variable Identification](EDA_Variable_Identification.md)
  * [2. Univariate Analysis](EDA_Univariate_Analysis.md)
  * [3. Bi-variate Analysis](EDA_Bi-variate_Analysis.md)
  * [4. Missing values treatment](EDA_Missing_values_treatment.md)
        - Why missing value treatment is required ?
        - Why data has missing values?
        - Which are the methods to treat missing value ?
  * [5. Outlier treatment](EDA_Outlier_treatment.md)
        - What is an outlier?
        - What are the types of outliers ?
        - What are the causes of outliers ?
        - What is the impact of outliers on dataset ?
        - How to remove outlier ?
  

• Feature Engineering: extracting more information from existing data

  - What is Feature Engineering ?
  - What is the process of Feature Engineering ?
  * [6. Variable transformation](EDA_Variable_transformation.md)
        - What is Variable Transformation ?
        - When should we use variable transformation ?
        - What are the common methods of variable transformation ?
  * [7. Variable creation](EDA_Variable_creation.md)
        - What is feature variable creation and its benefits ?
  

• A Complete Tutorial which teaches Data Exploration in detail

1. Steps of Data Exploration and Preparation

• Data Exploration: understand and clean your data

  * [1. Variable Identification](EDA_Variable_Identification.md)
  * [2. Univariate Analysis](EDA_Univariate_Analysis.md)
  * [3. Bi-variate Analysis](EDA_Bi-variate_Analysis.md)
  * [4. Missing values treatment](EDA_Missing_values_treatment.md)
  * [5. Outlier treatment](EDA_Outlier_treatment.md)
  

• Feature Engineering: extracting more information from existing data

  * [6. Variable transformation](EDA_Variable_transformation.md)
  * [7. Variable creation](EDA_Variable_creation.md)
  

2. Missing Value Treatment

    - Why missing value treatment is required ?
    - Why data has missing values?
    - Which are the methods to treat missing value ?

3. Techniques of Outlier Detection and Treatment

  - What is an outlier?
  - What are the types of outliers ?
  - What are the causes of outliers ?
  - What is the impact of outliers on dataset ?
  - How to remove outlier ?

4. The Art of Feature Engineering

  - What is Feature Engineering ?
  - What is the process of Feature Engineering ?
  - What is Variable Transformation ?
  - When should we use variable transformation ?
  - What are the common methods of variable transformation ?
  - What is feature variable creation and its benefits ?

Reference:

• A Complete Tutorial which teaches Data Exploration in detail
• Predictive and Descriptive Analytics in R - Grasp the entire concept thoroughly! - DataFlair
• R語言自學系列(4) - 資料清洗與探索性資料分析 - R 語言自學系列 - Medium
• R 資料探索與基本繪圖 - G. T. Wang
• A Complete Tutorial which teaches Data Exploration in detail
• R 資料探索與基本繪圖 - G. T. Wang
• 如何探索資料：基礎視覺化 – DataInPoint – Medium
• 如何探索資料：視覺化中的元件 – DataInPoint – Medium
• 如何探索資料：其他視覺化類型 – DataInPoint – Medium
• Johns Hopkins Exploratory Data Analysis
  • 家庭電表資料探索 – Johns Hopkins Exploratory Data Analysis 的第一週作業：Electric Power Consumption - DataInPoint – Medium
  • PM2.5 污染源資料探索 – Johns Hopkins Exploratory Data Analysis 的第四週作業：Fine particulate matter - DataInPoint – Medium
• Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data - Articles - STHDA
• A Complete Tutorial which teaches Data Exploration in detail