Exploratory Data Analysis (EDA) is like taking a first glance at an unexplored territory through binoculars.
It helps you orient yourself and determine where to go next.
In the world of data, EDA assists analysts and researchers in understanding the dataset by identifying key features, intriguing patterns, and potential anomalies.
What is EDA?
At its core, exploratory analysis is based on the idea that before building complex models or making final conclusions, one should thoroughly examine the data.
This includes visualization, statistical analysis, assumption checking, and hypothesis formulation based on observed patterns.
EDA is a dialogue between the analyst and the data, where the analyst asks questions and seeks answers within the structure and content of the data.
Consider this analogy: imagine you’ve just arrived in a large, unfamiliar city. Your task is to understand its layout, locate major landmarks, and identify districts worth visiting and those to avoid. Exploratory Data Analysis is very similar to this process.
Why is Exploratory Data Analysis Necessary?
EDA is foundational to data work. It not only provides a comprehensive understanding of the necessary information but also identifies potential obstacles to deriving meaningful insights.
EDA is needed for:
- Understanding the data: Before making decisions or building models, it’s crucial to understand your data’s main characteristics and structure.
- Data cleaning: EDA helps identify errors and anomalies in the data that could distort analyses, such as missing values or outliers.
- Hypothesis formulation: Based on observed patterns, hypotheses can be formulated for further testing in the analytical process.
- Model selection: Understanding the data allows for the selection of the most suitable statistical models and analysis methods.
Where and how is EDA used?
- EDA is employed for analyzing financial data, including stock prices, consumer behavior, and market trends. Fintech companies use EDA to understand customer preferences and behaviors, detect potential fraud, and make informed business decisions.
- In e-commerce, EDA is used to analyze transaction data and customer behavior. This helps identify the most successful products and features, and understand customer preferences.
- In marketing, EDA is used to analyze customer data, such as demographics, purchase history, and behavior. This aids in market segmentation, understanding customer preferences, and refining marketing strategies. EDA is also utilized for analyzing social media data, such as user behavior and trends, helping to understand user preferences and improve social media strategies.
- EDA is also applied in analyzing manufacturing data, including equipment operation, quality control, and inventory management. This helps identify inefficiencies, improve production processes, and reduce costs.
EDA Tools and Methods
Data Visualization
Through graphical representations and charts, data visualization reveals patterns, dynamics, and relationships among data.
For example, a scatter plot is a chart where each point represents an individual observation and illustrates the relationship between two variables. This type of infographic helps experts identify dependencies or correlations between variables.
A histogram is a chart where each point represents an individual observation and shows the relationship between two variables. Histograms help understand how often values fall into specific ranges and reveal peaks or dips in the data.
A box plot, or “box and whiskers” graph, visually represents statistical parameters of data distribution, including the median, quartiles, and outliers. This tool effectively analyzes data variability and symmetry.
Statistical Analysis
This helps quantitatively assess the main characteristics of data:
- Mean: Calculated as the total sum of all numbers in the set divided by their count, reflecting the “average” point.
- Median: The middle value of a dataset, or the average of the two middle values if the dataset size is even.
- Mode: Indicates the number that appears most frequently in a dataset, serving as an indicator of the most common or typical value.
Heatmaps
A heatmap displays data as a color matrix, where different colors signify the relationship between various elements. This simplifies the detection of patterns and interdependencies in extensive data.
Correlation Analysis
Correlation analysis identifies the relationships between variables and their strength. A correlation coefficient shows how one variable is linearly related to another:
- With positive correlation, both variables change in the same direction, and the coefficient is between 0 and 1.
- With negative correlation, the variables move in opposite directions, and the coefficient is between 0 and –1.
- If the correlation is zero, it means there is no linear relationship between the variables, and the coefficient is close to 0.
Data Transformation (Standardization and Normalization)
Data transformation involves adjusting the scale or distribution shape of variables to fit analytical and modeling procedures. This crucial part of EDA ensures the comparability of variables and facilitates the formation of data suitable for analysis and interpretation.
- Normalization adjusts variable values so that they range from 0 to 1, which is particularly valuable for variables with different units of measurement or scales.
- Standardization transforms variable values so that their mean becomes 0 and standard deviation –1, making the data distribution more uniform and balanced.
Anomaly and Outlier Analysis
This process helps identify data values that significantly differ from other observations. Anomalies arise from errors, random events, or characteristics of the phenomenon being studied.
Key steps in processing outliers and anomalies include:
- Visual analysis: Use graphical methods, such as box plots or scatter plots, to visually detect potential anomalies.
- Statistical testing: Identify anomalous values using statistical tools, based on criteria and evaluations of the study.
- Choosing a strategy: Determine how to handle anomalies—exclude them, adjust them, or leave them unchanged, depending on the context and goals of the study.
Summary: What is Exploratory Data Analysis?
While all this may sound complex, let’s return to our analogy and compare EDA to exploring a new city.
City map = data visualization. Just as you use a map to navigate a city, in EDA you use visualizations (charts, diagrams) to better understand data distribution and relationships.
Walking around the city = data exploration. As you move through the city, you take in the architecture, people, and overall atmosphere. In EDA, you “walk” through the data, exploring its characteristics, searching for patterns and anomalies.
Talking with locals = hypothesis testing. Interacting with local residents, you can learn more about the city and test your assumptions. In EDA, hypotheses are tested using collected statistics.
Exploratory analysis is critically important for deeply understanding data, identifying key trends, and preparing information for further analysis. The EDA process values not only a technical approach to analysis but also an intuitive understanding of data and its context.
