*Exploratory Data Analysis(EDA) * is the process of analyzing data using visual techniques that help us gain insights from data. Data visualization reveals patterns, trends, and relationships within the data. It involves creating charts, graphs, and plots to transform complex data into easily understandable visuals.
Data Visualization is effective in EDA for the following reasons:
Pattern recognition- Visualizations make it easier to identify patterns and relationships within the data aiding in hypothesis generation and validation.
Anomaly Detection- It highlights outliers or unusual data points, prompting further investigation.
Simplifies Complexity - simplifies data by presenting information in a visual format that's easy to comprehend.
Enhanced communication- Visual representations make it easier to convey findings and insights to stakeholders.
-Time efficiency- They provide an overview of data which saves time as compared to manually inspecting raw data.
Types of Exploratory Data Analysis
Bar Charts- Used to show comparisons between different categories.
Line Charts- Used to show trends over time or across different categories.
Pie Charts- Shows proportions/percentages.
Histograms- Shows the distribution of a single variable.
Heatmaps- Shows correlation between variables
Scatter Plots- Shows the relationship between two continuous variables
Box Plots- identifies outliers and shows the distribution of a variable
Top comments (1)
This is awesome, I've been looking into Data Engineering related post lately. Cheerios!