Exploratory Data Analysis - EDA is a crucial step in data science. Think of EDA as an investigation process, where you examine and explore your dataset to gain more insights on its characteristics, detect anomalies and so on.
One of the many ways to perform EDA is through data visualization.
Data Visualization is a component of EDA that allows analyst to understand their data. It makes complex data more understandable and helps in data driven decision.
In layman's word, data visualization is like using pictures and charts to tell a story about your data. By making data visually appealing and accessible, data visualization helps people make better decisions based on the information they have.
There are some data visualization techniques that are used for EDA. The choice of which techniques will be used depends on your data and the solution to your question.
These techniques are:
Histograms -
Histograms are useful for understanding the distribution of a single variable. They show you the shape of the data, whether it's skewed to the left or right, or if it's roughly symmetric. This helps you identify patterns and outliers in your data, which is crucial for making informed decisions in data science.
Scatter Plots -
Scatterplots are excellent for exploring the relationship between two variables. They help you understand if there's a correlation between them, whether they move together, or if they're independent.
Bar Graphs -
Bar graphs are perfect for comparing different categories or groups. For example, you can use a bar graph to show the sales of various products in a store, with each bar representing a different product. The taller the bar, the higher the sales for that product. Bar graphs are great for making data-driven decisions when you need to compare the sizes or quantities of different categories or groups.
Box Plots -
Box plots are great for visualizing the distribution of data and identifying outliers. They help you understand the spread of your data, its central tendency, and whether there are any unusual values that might need further investigation.
Heat Maps
Heatmaps are particularly useful for visualizing the relationships between multiple variables. They represent data in a grid format, with colors indicating the strength of the relationships. It is used to visualize complex data, particularly when dealing with large datasets or matrices.
Violin Plots
A violin plot is a data visualization that combines elements of a box plot and a kernel density plot. It is often used to depict the distribution and summary statistics of a dataset, providing a more detailed view of data distribution than a simple box plot.
Density Plots
A density plot, also known as a kernel density plot, is a data visualization technique used to estimate and display the probability density function of a continuous random variable. In simpler terms, it provides a smoothed, continuous representation of the data's distribution, making it easier to understand the shape and characteristics of the distribution.
Spider Plots
A spider plot, also known as a radar chart or spider chart, is a data visualization technique that displays multivariate data in a two-dimensional graphical form. It is particularly useful for comparing multiple variables across different categories or groups.
There are others like Contour plots, Probability Distribution Plots, Tree Maps Pair plots and so on. These data visualization techniques are used depending on your data analysis.
Now, we have tools used for creating these data visualizations. They are:
MatPlotLib, Seaborn, Plotly - Python Libraries
ggplot2, Shiny - R Libraries
Tableau, Power BI - Business Intelligence Tools
QlikView/Qlik Sense, Looker - Data Visualization Software
The tools used depends on your depends on the complexity of your project and how you familiarize yourself with them. It's important to explore and experiment with different tools to find the one that best fits your specific project and skill level.
In conclusion, data visualization is a creative and powerful means of exploring and understanding data. By asking the right questions, comprehending your project's context and dataset, and employing your creativity, you can harness the full potential of data for informed decision-making.
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