Although there is no universally accepted approach to beginning a data analysis effort, it is typically a good idea to develop a formal process for yourself when first examining a dataset. This routine can manifest itself as a dynamic checklist of tasks that evolves as your data exploration skills progress.

Exploratory Data Analysis (EDA) is a term used to encompass the entire process of analyzing data without the formal use of statistical testing procedures. Much of EDA involves visually displaying different relationships among the data to detect interesting patterns and develop hypotheses.

In this webinar, we’ll systematically undertake a routine to explore a real-world messy dataset. We will use the Pandas Python library to transform, clean, and analyze our data as well as the Seaborn library to create beautiful visualizations. By the end of the demonstration, you will have a detailed checklist of tasks that you can use, or customize to your liking, to thoroughly explore any dataset.

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