Demystifying the Exploratory Data Analysis (EDA) Process — Part II
<p>As outlined in <a href="https://medium.com/@idatawiz/demystifying-the-exploratory-data-analysis-eda-process-part-i-53e169ed72ec" rel="noopener">Part I</a>, in this series we adopt a hybrid approach to data exploration. In the previous article, we saw how we could leverage SQL for preliminary data analysis. In this section, we delve into Python to continue our analysis with data visualization.</p>
<p>I’d like to emphasize once more that there have been significant advancements in Exploratory Data Analysis (EDA) tools, which greatly simplify the often laborious process by reducing the need for extensive coding. However, in my opinion, having a solid grasp of the fundamentals is essential to fully appreciate the capabilities these tools offer.</p>
<p>It’s crucial to recognize that the thought process involved in exploring datasets remains consistent, regardless of the tool you employ. While automated EDA tools can assist in data exploration, they may not guide you toward the critical aspects in data that require investigation. The ability to discern what questions to focus on and investigate is a skill honed through practical experience.</p>
<p><a href="https://medium.com/@idatawiz/demystifying-the-exploratory-data-analysis-eda-process-part-ii-67a5f9608312"><strong>Read More</strong></a></p>