Introduction
Exploratory Data Analysis (EDA) is the beginning of data analysis. Data scientists use it to analyze and investigate datasets and come up with summaries of their main characteristics. While implementing EDA, data visualization is one of the most powerful tools at our disposal in that visualization allows us to represent data visually thus making it easier for data scientists to discover patterns, spot anomalies, test hypothesis, or check assumptions. This allows us to gain insight that might have been difficult to obtain from raw numbers alone.
Types of exploratory data analysis
There are four primary types of EDA:
• Univariate non-graphical. In this form of analysis, there consists of only one variable in the data being analyzed and thus one does not have to deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.
• Univariate graphical. Graphical methods are required since non-graphical methods do not provide a full picture of the data. Common types of univariate graphics include:
o Stem-and-leaf plots, which show all data values and the shape of the distribution.
o Histograms, a bar plot in which each bar represents the frequency (count) or proportion (count/total count) of cases for a range of values.
o Box plots, which are based on percentiles and give a quick way to visualize data distribution.
• Multivariate non-graphical: This type of analysis is implemented on data that contains more than one variable. The EDA techniques executed generally show the relationship between two or more variables of the data through cross-tabulation or statistics.
• Multivariate graphical: Uses graphics to display relationships between two or more sets of data. The most used graphic is a grouped bar plot or bar chart with each group representing one level of one of the variables and each bar within a group representing the levels of the other variable.
Data Visualization
In EDA, data visualization is the process of representing data graphically to reveal patterns, trends, and relationships within the data. It involves creating charts, graphs, and plots to transform complex data into easily understandable visuals
Why Data Visualization?
• Simplifies Complexity: Data can be overwhelming and complex, especially when dealing with large datasets. Visualization transforms this data into charts, graphs and diagrams that are easy to comprehend.
• Pattern Recognition: By presenting data in a visual format, it becomes easier to identify patterns and relationships within the data thus aiding in hypothesis generation and validation.
• Enhanced Communication: Visual representations of data offers a more accessible and engaging way of communicating, making it simpler to convey findings and insights to stakeholders.
• Anomaly Detection: Visualization tools often include features that can quickly highlight outliers or unusual data points, prompting further investigation.
• Time Efficiency: Visualization tools provide a quick way to gain a rapid overview of the data thus saving time compared to manually inspecting raw data.
Common Data Visualization Techniques
There is a myriad of data visualization techniques available, each suited to specific data types and objectives. Many require setting up, maintaining and using elaborate BI tools with limited capabilities. Python, The number one language in data science, however, offers a better way to tackle visualization. Python offers a wide visualization library from matplotlib to plotly to seaborn that implement the following data visualization techniques in order to communicate insights to stakeholders:
1. Scatter Plots
Scatter plots display individual data points as dots on a two-dimensional plane. They are excellent for visualizing the relationship between two paired sets of data.
2. Histograms
Histograms display the distribution of a single variable's values. They are useful for understanding the data's central tendency, spread, and shape.
3. Bar Charts
Bar charts represent data with rectangular bars, making them ideal for comparing categorical data. They are often used for visualizing frequencies, proportions, or rankings.
4. Line Charts
Line charts connect data points with lines, showing how a variable changes over a continuous range. They are useful for displaying trends over time.
5. Box Plots
Box plots provide a visual summary of the distribution of a dataset. They show the median, quartiles, and potential outliers, making them valuable for identifying data skewness and variability.
6. Heatmaps
Heatmaps use colors to represent the values in a two-dimensional matrix. They are valuable for visualizing correlations or patterns in large datasets.
Tools for Data Visualization
To create effective data visualizations, you'll need the right tools. Some popular data visualization tools include:
• Python Libraries: Matplotlib, Seaborn, Plotly, and Pandas are popular libraries for data visualization in Python.
• R: R is a programming language specifically designed for data analysis and visualization, with packages like ggplot2.
• Tableau: A powerful data visualization tool with a user-friendly interface.
• Power BI: Microsoft's Power BI allows users to create interactive and visually appealing reports and dashboards.
Top comments (0)