This is Part I in a 2-part series of some explorations of data tools and approaches to analysis. This part of the series had began as an attempt to perform some exploratory analysis on a given dataset. However as I progressed through the analysis, I realized the small nuances and details at various stages of this analysis which might lead us to draw very different conclusions from the same data. The report attached in the repository draws on these nuances to detail some caveats and insights on exploratory data analysis (EDA) performed through Python and R and provides a detailed explanation of the process. In Part II of the series, I will build my own dataset through API calls to websites and carry out an EDA while keeping track of these nuances and caveats.
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