The “Data Science & AI Interview Question Series” is a valuable resource for freshers and experienced data science professionals preparing for job interviews. It provides a comprehensive guide to essential data science concepts, techniques, and practical applications. By studying this series, candidates can gain a deeper understanding of data science principles, improve their problem-solving skills, and increase their confidence in interview settings. Whether you’re a new graduate or a seasoned data scientist, this series can help you succeed in your data science career.
This document focuses on “Data Cleaning” in the context of data science and AI. It covers various aspects of data cleaning, including identifying and handling common data quality issues such as missing values, duplicates, outliers, and inconsistent formats. It also explores strategies for automating data cleaning processes and best practices for effective data cleaning. The document provides easy-to-understand explanations and detailed explanations to cater to different learning preferences. Whether you’re new to data science or an experienced practitioner, this guide serves as a quick reference for essential data cleaning techniques and best practices, helping you ensure high-quality data for your data analysis and machine learning tasks.
Questions Covered:
𝟏. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐜𝐫𝐮𝐜𝐢𝐚𝐥 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞?
𝟐. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬𝐬𝐮𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐚𝐝𝐝𝐫𝐞𝐬𝐬𝐞𝐬?
𝟑. 𝐖𝐡𝐚𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐮𝐬𝐞 𝐭𝐨 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐝𝐚𝐭𝐚?
𝟒. 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚𝐧𝐝 𝐡𝐚𝐧𝐝𝐥𝐞 𝐝𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞 𝐫𝐞𝐜𝐨𝐫𝐝𝐬 𝐢𝐧 𝐚 𝐝𝐚𝐭𝐚𝐬𝐞𝐭?
𝟓. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐨𝐮𝐭𝐥𝐢𝐞𝐫𝐬, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐝𝐞𝐚𝐥 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐦 𝐢𝐧 𝐚 𝐝𝐚𝐭𝐚𝐬𝐞𝐭?
𝟔. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐫𝐨𝐥𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬?
𝟕. 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐡𝐚𝐧𝐝𝐥𝐞 𝐢𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐝𝐚𝐭𝐚 𝐟𝐨𝐫𝐦𝐚𝐭𝐬 𝐝𝐮𝐫𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠?
𝟖. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬?
𝟗. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐝𝐚𝐭𝐚 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐫𝐞𝐥𝐚𝐭𝐞 𝐭𝐨 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠?
𝟏𝟎. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐛𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠?
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