Exploratory Data Analysis: The Ultimate Workflow

Are you tired of starting from scratch every time you need to explore your data, without a clear roadmap? Look no further! I will guide you through a step-by-step process using Python to uncover valuable insights and trends hidden in your data. Whether you’re a beginner or an experienced data analyst, this article has something for you. We will look at different ways in which you can explore your dataset, depending on your end goal, with clear steps and objectives for each of them. Disclaimer: we will focus on tabular data only, so if you need a similar process for images, text, etc. this article is not for you. I might do a similar one for those types of data in the future, let me know in the comments if this would interest you. <h1>Introduction</h1> The first thing we need to have in mind when exploring data is why we are doing it. There are multiple reasons to explore a dataset, the main ones usually being: <ul> <li>understanding your data before building an ML model</li> <li>analysis to uncover interesting patterns</li> <li>sheer curiosity</li> </ul> Depending on your goal, the analysis can take slightly different forms, but the basic structure I use is this: <ol> <li>Loading libraries and data</li> <li>Reading data documentation</li> <li>Univariate data analysis</li> <li>Bivariate data analysis</li> <li>Multivariate data analysis</li> <li>Insights and next steps</li> </ol> If your goal is to build an ML model in the end, you will dedicate more time exploring the target variable. If your goal is pure curiosity, on the other hand, your approach can be much less structured. <a href="https://levelup.gitconnected.com/exploratory-data-analysis-the-ultimate-workflow-a82b1d21f747">Read More</a>