It is often said that "a picture is worth a thousand words".In today's data-driven world, information is abundant, but insights are priceless. To unveil the hidden gems within your datasets, one must embark on a journey of data exploration.
Data Visualization also helps data analysts to consider what's important to show and what isn't.
Visualization is a means to understand patterns in data,identify outliers and other points of interest and findings to an outside audience without having to sift through data manually.
It helps us glean information from raw data and gain insights that would otherwise be difficult to draw.
Data visualization is an essential part of Exploratory Data Analysis, as it helps to analyze and visualize the data to gain enlightening insights into its distribution, relationships between variables, and potential outliers.
Visualization is really important in telling a clear and concise story to stakeholders.
Data is a treasure chest, and data visualization is the legend that can unlock its secrets. Data collecting is not enough; it's the process of exploration and interpretation that transforms raw data into actionable knowledge.
Steps to perform EDA using data visualization techniques
1. Data Collection and Loading:
Begin by gathering and loading your dataset. Data can be in various formats, such as CSV, Excel, or databases.
2. Data Cleaning:
Check for missing values, duplicates, and outliers. Address any data quality issues to ensure your analysis is based on reliable data.
3. Univariate Analysis:
Explore individual variables separately.
Use these visualization techniques for univariate analysis:
Histograms: Visualize the distribution of numeric variables to understand their shape (normal, skewed, bimodal).
Bar Charts: Use them to display the frequency of categories in categorical variables.
Box Plots: Identify outliers and understand the spread of numeric data.
4. Bivariate Analysis:
Investigate relationships between pairs of variables.
Employ these visualization techniques for bivariate analysis
Scatter Plots: Display the relationship between two numeric variables to uncover correlations or patterns.
Heatmaps: Visualize the correlation matrix to understand associations between variables.
Cross-tabulations and Stacked Bar Charts: Compare categorical data across different categories of another variable.
5. Multivariate Analysis:
Examine interactions and dependencies among multiple variables.
Use these visualization techniques for multivariate analysis:
- Pair Plots or Scatterplot Matrices: For a quick overview of relationships between multiple numeric variables.
- Parallel Coordinate Plots: Visualize patterns in high-dimensional datasets.
- 3D Plots: If necessary, use 3D visualizations for exploring relationships in three-dimensional space.
Summary
Univariate analysis looks at one variable, Bivariate analysis looks at two variables and their relationship. Multivariate analysis looks at more than two variables and their relationship.
By systematically following the steps outlined in this approach, one can gain a deep understanding of the dataset, identify patterns, relationships, and outliers, and ultimately make informed decisions based on data-driven insights.
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