Although there is no standard approach when beginning a data analysis, it is typically a good idea to develop a routine for yourself when first examining a dataset. Similar to common routines that we have for waking up, showering, going to work, eating, and so on, a beginning data analysis routine helps one quickly get acquainted with a new 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.