A Data Scientist’s Essential Guide to Exploratory Data Analysis
<p>Exploratory Data Analysis (EDA) is the single most important task to conduct at the beginning of every data science project.</p>
<p>In essence, it involves thoroughly examining and characterizing your data in order to find its underlying <strong>characteristics</strong>, possible <strong>anomalies</strong>, and hidden <strong>patterns</strong> and <strong>relationships</strong>.</p>
<p>This understanding of your data is what will ultimately <strong>guide through the following steps</strong> of you machine learning pipeline, from data preprocessing to model building and analysis of results.</p>
<h2>The process of EDA fundamentally comprises three main tasks:</h2>
<ul>
<li><strong>Step 1:</strong> <em>Dataset Overview and Descriptive Statistics</em></li>
<li><strong>Step 2:</strong> <em>Feature Assessment and Visualization</em>, and</li>
<li><strong>Step 3:</strong> <em>Data Quality Evaluation</em></li>
</ul>
<p>As you may have guessed, each of these tasks may entail a quite comprehensive amount of analyses, which will easily have you<em> slicing, printing, and plotting your pandas dataframes like a madman.</em></p>
<p><a href="https://towardsdatascience.com/a-data-scientists-essential-guide-to-exploratory-data-analysis-25637eee0cf6">Read More</a></p>