10 Questions on Exploratory Data Analysis (EDA) in Data Science and AI Interviews

The “Data Science & AI Interview Question Series” is a valuable resource for both freshers and experienced data science professionals preparing for job interviews. It provides a comprehensive guide to answering common interview questions on various data science topics, including Exploratory Data Analysis (EDA).

By studying these question-answer series, candidates can gain a deeper understanding of core concepts, refresh their knowledge, and develop the confidence to tackle challenging interview scenarios.

This document focuses on Exploratory Data Analysis (EDA), a critical process in data science for understanding and preparing data for modeling. It covers key steps in EDA, including data cleaning, handling missing values, detecting outliers, and using graphical and non-graphical techniques.

The document also highlights common challenges faced during EDA and provides solutions to overcome them. By following the guidance and examples provided, readers can gain a solid understanding of how to perform effective EDA and leverage its insights for successful machine learning model development.

The key questions covered today:

𝟏.   𝐖𝐡𝐚𝐭 𝐢𝐬 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐨𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐄𝐃𝐀) 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭?

𝟐.   𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐬𝐭𝐞𝐩𝐬 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐝 𝐢𝐧 𝐄𝐃𝐀?

𝟑.   𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐮𝐬𝐞𝐝 𝐢𝐧 𝐄𝐃𝐀?

𝟒.   𝐇𝐨𝐰 𝐜𝐚𝐧 𝐧𝐨𝐧-𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐚𝐥 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐛𝐞 𝐮𝐬𝐞𝐝 𝐢𝐧 𝐄𝐃𝐀?

𝟓.   𝐖𝐡𝐲 𝐢𝐬 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐄𝐃𝐀, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐢𝐭?

𝟔.   𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐧𝐠 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬 𝐢𝐧 𝐄𝐃𝐀?

𝟕.   𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐝𝐮𝐫𝐢𝐧𝐠 𝐄𝐃𝐀?

𝟖.   𝐇𝐨𝐰 𝐜𝐚𝐧 𝐄𝐃𝐀 𝐡𝐞𝐥𝐩 𝐢𝐧 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐚 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥?

𝟗.   𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐲𝐨𝐮 𝐟𝐚𝐜𝐞 𝐝𝐮𝐫𝐢𝐧𝐠 𝐄𝐃𝐀 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐨𝐯𝐞𝐫𝐜𝐨𝐦𝐞 𝐭𝐡𝐞𝐦?

𝟏𝟎.   𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐄𝐃𝐀 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞 𝐭𝐨 𝐭𝐡𝐞 𝐨𝐯𝐞𝐫𝐚𝐥𝐥 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰?

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