Exploratory Data Analysis (EDA) is a critical data analysis process as it involves understanding and identifying patterns in the data. EDA processes include studying the data to discover patterns, identify how variables are related, and locate outliers.
Why perform EDA?
- To identify patterns in the data. By visualizing the data and checking the statistical summaries of the numerical variables, one can see the hidden patterns in the data and also how some variables are related.
- To detect outliers and anomalies. Outliers are values in columns that are abnormally far from the rest of the values. Outliers can greatly affect the results of the analysis and as a result, detecting and handling them is key in reducing the chances of mistakes occurring in the data modelling or prediction process.
- To facilitate data cleaning. Through EDA, one can spot issues in the data like missing values and errors and this can inform how the data can be cleaned.
- To understand the data structures in the data. With EDA, you can get a better understanding of the features and their distribution and this can help inform how the data analysis and feature engineering will be done.
Techniques in EDA
Univariate analysis
Univariate analysis is the analysis of a single variable. The purpose of univariate analysis is to understand the summary statistics and distribution of the variable. Some of the activities in the univariate analysis include summary statistics and visualizing the data using histograms, box plots, bar charts, line plots and violin plots among others.
Bivariate analysis
Bivariate analysis refers to the analysis of how two variables are related. This analysis helps in uncovering patterns in the data and the commonly used bivariate analysis techniques are pair plots, heatmaps and scatter plots. Other techniques include line graphs, cross-tabulation and covariance
Multivariate analysis
Multivariate analysis is the simultaneous examination of the relationships between more than two variables. The aim of this analysis is to understand how the various features in the dataset are interacting. Commonly used multivariate analysis techniques include principal component analysis, pair plots and contour plots.
Statistical tests
Statistical tests help in validating hypotheses and discerning significant differences between groups. Some of the statistical tests when performing EDA include t-tests, ANOVA, and chi-square tests.
Conclusion
EDA is an important step in the data analysis or data science pipeline. In EDA, you can use techniques like multivariate, bivariate, univariate and even statistical tests to unlock hidden insights in the data.
If done well, EDA can help a data professional make their data cleaner, more accurate and finally to make better performing models. As a data professional, embracing best practices in EDA is important in understanding your dataset and ultimately generating reliable insights from the data.
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