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Exploratory Data Analysis (EDA) is the process of analyzing a dataset for the purpose of identifying, describing and visualizing patterns. It is one of the most important steps in data science because it helps us understand our data better and make better decisions about how to proceed with it. The main goal of EDA is to get an overview of your data using different methods such as histograms and boxplots, so that you can quickly identify any potential issues with your dataset, such as missing values or outliers. You can also use EDA to visualize your data so that you can understand it better. Read More: https://hsndigitally.com/what-is-exploratory-data-analysis-in-data-science/
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Exploratory data analysis is a crucial step in creating a data visualization. In this video, I'll go over the four main types of exploratory data analysis: univariate, bivariate, multivariate and time series. Each one of these types has a different goal—univariate is used to get an overview of your data; bivariate is used to explore relationships between variables; multivariate is used to identify patterns within subgroups; and time series allows you to look at changes over time. To learn more about how you can use these tools to get a better understanding of your data, so that you can make the most informed decisions possible when creating a visual presentation for your audience, visit the link below. To read more visit: https://hsndigitally.com/what-is-exploratory-data-analysis-in-d
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