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Exploratory Data Analysis

Data Analytics Concept

Exploratory Data Analysis (EDA) is a fundamental process in data analysis that involves the initial exploration of data to uncover hidden patterns, anomalies, and relationships. EDA is a systematic approach that uses summary statistics and graphical representations to develop a deeper understanding of the data and identify potential issues.

Key Highlights

  • EDA is a critical step in the data analysis process that enables analysts to identify trends, patterns, and relationships in the data.
  • EDA involves the use of statistical methods and visualization techniques to analyze data and develop insights.
  • EDA is an iterative process that involves continuous refinement of analysis based on new insights.


How to Apply EDA to Business

EDA is an essential tool for businesses to gain insights into their data. By performing EDA, businesses can make informed decisions based on data-driven insights. EDA can be used to identify customer preferences, market trends, and potential issues with products or services. EDA can also be used to evaluate the performance of marketing campaigns, identify areas for improvement in operational processes, and optimize business operations. By leveraging EDA, businesses can make data-driven decisions that lead to improved performance, enhanced customer satisfaction, and increased profitability.

  • Exploratory Data Analysis
    • Data Cleaning
      • Missing Value Treatment
      • Outlier Detection
      • Noise Reduction
      • Data Transformations
    • Data Profiling
      • Descriptive Statistics
        • Central Tendency: Mean, Median, Mode
        • Dispersion: Variance, Standard Deviation, Range, Interquartile Range
      • Data Types Identification: Categorical, Numerical, Ordinal, Interval, Ratio
      • Variable Identification: Predictor, Target, Identification
    • Data Visualization
      • Univariate Analysis: Histograms, Bar Charts, Box Plots, Density Plots
      • Bivariate Analysis: Scatter Plots, Pair Plots, Heatmaps
      • Multivariate Analysis: Parallel Coordinate Plots, Correlation Matrix
    • Statistical Analysis
      • Correlation Analysis
      • ANOVA: One-way ANOVA, Two-way ANOVA
      • Chi-square Test
    • Dimensionality Reduction
      • Principal Component Analysis (PCA)
      • t-Distributed Stochastic Neighbor Embedding (t-SNE)
      • Linear Discriminant Analysis (LDA)
    • Feature Engineering
      • Feature Selection: Filter, Wrapper, Embedded Methods
      • Feature Extraction
      • Feature Scaling: Standardization, Normalization
    • Hypothesis Testing
      • Null Hypothesis
      • Alternative Hypothesis
      • p-Value
      • Confidence Interval
    • Modelling
      • Building Models
      • Evaluating Models
      • Optimizing Models
      • Interpret Models
    • Reporting
      • Reporting Findings
      • Visual Reporting
      • Interactive Dashboards
      • Storytelling with Data