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The 7 Most Useful Jupyter Notebook Extensions for Data Scientists

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By Cornellius Yudha Wijaya , KDnuggets Technical Content Specialist on June 18, 2025 in Data Science Image by Author As a data scientist, Jupyter Notebook has become one of the first platforms we learn to use, as it allows for easier data manipulation compared to standard programming IDEs.

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Why You Need RAG to Stay Relevant as a Data Scientist

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By Nate Rosidi , KDnuggets Market Trends & SQL Content Specialist on June 11, 2025 in Language Models Image by Author | Canva If you work in a data-related field, you should update yourself regularly. Data scientists use different tools for tasks like data visualization, data modeling, and even warehouse systems.

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Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale

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By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale Top 7 MCP Clients for AI Tooling Why You Need RAG to Stay Relevant as a Data Scientist Stop Writing Messy Python: A Clean Code Crash Course Selling Your Side Project?

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Selling Your Side Project? 10 Marketplaces Data Scientists Need to Know

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Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Selling Your Side Project?

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Go vs. Python for Modern Data Workflows: Need Help Deciding?

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Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding?

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Automate Data Quality Reports with n8n: From CSV to Professional Analysis

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Whats the overall data quality score? Most data scientists spend 15-30 minutes manually exploring each new dataset—loading it into pandas, running.info() ,describe() , and.isnull().sum() sum() , then creating visualizations to understand missing data patterns. Which columns are problematic?

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Run the Full DeepSeek-R1-0528 Model Locally

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Abid Ali Awan ( @1abidaliawan ) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies.