# A Five-Step Guide for Conducting Exploratory Data Analysis

DevFeed: [A Five-Step Guide for Conducting Exploratory Data Analysis](<https://devfeed.tech/articles/a-five-step-guide-for-conducting-exploratory-data-analysis-1352.md>)

Original publisher: [Read original article](<https://shopify.engineering/conducting-exploratory-data-analysis>)

Author: Cody Mazza-Anthony

Published: 2021-04-28T19:43:52Z

Content type: tutorial

Language: en

Sources: [Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering.md>), [Shopify Engineering - Shopify Engineering](<https://devfeed.tech/sources/shopify-engineering-shopify-engineering.md>)

Topics: [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [Jupyter Notebook](<https://devfeed.tech/topics/jupyter-notebook.md>), [Shopify](<https://devfeed.tech/topics/shopify.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [context](<https://devfeed.tech/tags/context.md>), [data-analysis](<https://devfeed.tech/tags/data-analysis.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [features](<https://devfeed.tech/tags/features.md>), [framework](<https://devfeed.tech/tags/framework.md>), [guide](<https://devfeed.tech/tags/guide.md>), [jupyter-notebook](<https://devfeed.tech/tags/jupyter-notebook.md>), [shopify](<https://devfeed.tech/tags/shopify.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [tips](<https://devfeed.tech/tags/tips.md>)

## AI overview

A five-step guide to exploratory data analysis (EDA), covering how to understand a dataset, identify patterns such as correlations, missing data, and outliers, and use visualizations and hypotheses to support business and data product decisions. It illustrates the process with a synthetic dataset and discusses practical considerations such as dataset size, computational limits, and working in a Jupyter notebook.

## Source excerpt

Have you ever been handed a dataset and then been asked to describe it? When I was first starting out in data science, this question confused me. My first thought was "What do you mean?" followed by "Can you be more specific?" The reality is that exploratory data analysis (EDA) is a critical tool in every data scientist's kit, and the results are invaluable for answering important business questions.