Data Exploration with Python, Part 1: Preparing Yourself to Become a Great Explorer
Exploratory data analysis (EDA) is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with. It is the exploratory analysis that gives us a sense of what additional work should be performed to quantify and extract insights from our data. It also informs us as to what the end product of our analytical process should be. Yet, in the decade that I've been working in analytics and data science, I've often seen people grasping at straws when it comes to exploring their data and trying to find insights.
In this post, we will introduce a framework for exploratory analysis and a way of thinking about data, both in general and for an example data set, that will help us explore our data in a creative but structured way. After reading this post, you should have gained foundational knowledge about the data set you're working with, as well as some data transformation and visualization methods available to you, so that you can quickly deploy them when working through the subsequent posts in this blog post series.