In today’s data-driven world, understanding data is important to obtain insights from information. While performing Data analysis task EDA helps data professionals understand the data in a visual and summarized form before applying complex statistical methods. Be it a beginner or professional data analyst or a data scientist, EDA is that crucial step in helping find patterns in data and detecting anomalies or outliers. It helps in Data Visualization tasks.
This tutorial covers various analysis methodologies, including univariate, bivariate, and multivariate analysis. It explains what EDA is, why it is important, and provides guidance on how to get started.
What is Exploratory Data Analysis?
Exploratory data analysis is a data analysis method used for analyzing, summarizing, and visualizing the data to get useful insights from data. EDA helps data science professionals to understand the data, identify data patterns, and relationships among different variables of the dataset.
We must perform EDA before applying any machine learning model or statistical techniques.
Why is Data Analysis EDA Important?
(1) Understand the data: It helps to understand the dataset. By doing EDA, we easily can evaluate the distribution of variables, find anomalies or outliers, and understand their relationship to one another.
(2) Enhance Data Cleaning Process: We identify errors and missing values in the data through EDA, and we must fix them before further analysis can enhance the data’s quality and integrity. A thorough analysis of the data can help us find regions that require transformation or cleaning.
(3) Identify relationship and Data Patterns: By visualizing the data, We get to know about data patterns and check the relationship between variables of data, how one variable relates to other variables.
(4) Feature Selection: By doing EDA we get to know which features are relevant to data and choose for model selection. For instance, we are carrying out some machine learning tasks. You should make feature selections based on the insights gained from EDA for building the machine learning model.
(5) Outlier and Anomaly Detection: Quality issues of data can be like duplicate entries, missed values, and inappropriate data entry that may cause harm to the analysis of data.
Types of Data Analysis in EDA
When carrying out EDA, you should divide the analysis into univariate, bivariate, and multivariate analyses. Such types of analyses allow you to explore several issues of the dataset depending on the number of variables under consideration.
(1) Univariate Analysis
One variable is examined at a time using univariate analysis. Its goal is to comprehend that specific variable’s distribution and properties.
For Numerical Variables : We use histograms, boxplots and frequency tables to understand the distribution of these numerical variables of dataset.
For Categorical Variables : The distribution of categorical variables shows by visual tool like bar plot and pie charts.
(2) Bivariate Analysis
Examining the relationship between variables is part of bivariate analysis. It enables the detection of dependencies, correlations, and the relationship between sets of variables. Bivariate analysis significantly explores the relationship between two variables as a form of exploratory data analysis.
Numerical versus Numerical Data: Relationships can be displayed visually with a scatter plot, and their strength and direction can be given a numerical value with a correlation coefficient like Pearson’s correlation.
For Numerical vs Categorical Data: Bar plot or box plot can show how a numerical value varies across categories.
For Categorical vs Categorical Data: The association between two category variables can be summarized using a heatmap or contingency table.
(3) Multivariate Analysis
Multivariate analysis expands upon bivariate analysis by considering three or more variables at once. When dealing with complex datasets that have nonlinear or complex connections between variables, this technique is quite helpful.
Pair plot: Use many variables to visualize relationships in order to get a complete picture of possible interactions.
Principal Component Analysis: This is a means of decreasing the dimensionality of this data to make it simpler to view and comprehend the relationships between several variables of the dataset.
Steps to perform EDA
- Understanding of Dataset: Load the dataset and examine it first. Examine the dimensions, column names, and types of data variables (numerical and categorical) you are working with.
- Missing Data Handling: To determine how to handle any missing data, use descriptive statistics or visualization to find the missing data. Replace the missing values by mean, median or mode.
- Summary Statistics: Calculate basic statistics such as mean, median, mode, standard deviation, and variance of the numerical variables of dataset.
- Visualization of data: You can se visual tools such as box plots, scatter plots, and histograms to gain a visual comprehension of the data.
- Outlier detection: Use visualization techniques to find outliers and determine how to handle or eliminate them. Utilize techniques such as the interquartile range (IQR), Z-scores, or region-specific laws to locate and examine potential outliers:
- Feature Engineering: You can enrich your dataset for analysis by adding new features or transforming current ones in light of your EDA findings.
- Finding and insights: The EDA approach concludes with a successful discussion of your discoveries and perspectives. This entails condensing your analysis, emphasizing key findings, and clearly and productively communicating your findings.
Tools and Libraries for performing EDA
- Python (Pandas, seaborn, matplotlib, plotly): When we perform Exploratory Data Analysis, we use theses libraries for data manipulation, analysis and visualization.
- R (ggplot2, dplyr): R is widely used for data visualization and manipulation because it offers robust statistical analysis features.
- Tableau and Power BI: These are powerful visualization tools or software for making interactive visualizations of the dataset.
Conclusion
Any data analysis project must begin with exploratory data analysis, or EDA. It helps in understanding your data, identifying mistakes, and revealing insights that will direct the remainder of your analysis. This would enable you to gain insight from your data: univariate, bivariate, and multivariate. EDA is always important, whether one is preparing a machine learning model or just in need of insights within his/her data. Data scientists can uncover hidden facts and guide initiatives toward success with the help of EDA.
