Exploratory Data Analysis (EDA) is a crucial step in the analysis of any dataset. It involves iterative processes to explore, discover, and summarize patterns within large datasets. EDA is used to understand and identify relationships between data, uncover anomalies, and gain insights into the data. It is often used to prepare data for further analysis, such as supervised learning, and to identify potential problems with data collection.
EDA can be used to examine data from many different perspectives. It involves visualizing the data, performing basic statistical analysis, looking for trends, understanding distributions, and modeling the data. Visualization is an important tool for EDA, as it can quickly reveal outliers and anomalies as well as complex structures and patterns in the data. These visualizations can be used to build hypotheses and prompt further investigation.
Statistical techniques such as t-tests, chi-square tests, and correlation analysis are commonly used to explore the data. These tests can help identify correlations between different variables, which can then be used to create predictive models. Further, these tests can be used to identify trends and anomalies in the data. Modeling is also used in EDA to identify patterns and associations in the data. This includes clustering and other unsupervised learning techniques.
EDA is an important tool for data science, as it allows for a deep understanding of the data before further analysis is conducted. It is a powerful technique for uncovering insights, uncovering potential problems, and generating hypotheses.
References
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Sommer, R. (2014). Exploratory data analysis: 5 practical examples. Data Science Central.
Wang, L., & Yao, X. (2009). Exploratory data analysis: an application-oriented approach. Wiley Interdisciplinary Reviews: Computational Statistics, 1(3), 315-321.