Data exploration is the initial step in data analysis where you inspect, summarize, and visualize raw data to understand its structure, spot patterns, and detect anomalies before building models.
Core Objectives
• Understand structure: Check data types, row/column counts, and missing values.
• Detect anomalies: Find outliers, duplicate records, or data entry errors.
• Uncover relationships: Identify correlations and dependencies between variables.
• Guide preparation: Inform decisions on data cleaning, scaling, and feature engineering.
Key Techniques
• Descriptive Statistics: Calculate central tendencies (mean, median, mode) and dispersion (variance, standard deviation, interquartile range).
• Univariate & Bivariate Analysis: Analyze single variables (histograms, boxplots) or compare two or more variables (scatter plots, heatmaps, pair plots).
• Correlation Analysis: Measure linear or rank relationships using methods like Pearson's coefficient.
• Dimensionality Reduction: Simplify complex datasets using techniques like Principal Component Analysis (PCA).
Common Tools & Languages
• Python: Libraries like Pandas, NumPy, Matplotlib, Seaborn, and Plotly.
• R: Built-in statistical functions alongside tidyverse and ggplot2.
• Business Intelligence (BI): Interactive dashboards via Tableau or Power BI.
https://www.geeksforgeeks.org/data-analysis/what-is-data-exploration-and-its-process/
https://www.ibm.com/think/topics/exploratory-data-analysis
https://towardsdatascience.com/a-data-scientists-essential-guide-to-exploratory-data-analysis-25637eee0cf6/
https://blog.ml.cmu.edu/2020/08/31/2-data-exploration/
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