Dr. Estera Kot

Data Science in Microsoft Fabric – Data preparation and exploration with Data Wrangler

Episode 19 of Fabric Espresso, with Dr. Estera Kot.

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Length 19:15. Published Aug 15, 2023 on the Azure Synapse Analytics YouTube channel. Topic: Data Science and Machine Learning.

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About this episode

Text from the YouTube description.

🌟📊 Exploring Data Science in Microsoft Fabric 📊🌟

Step into the dynamic realm of Microsoft Fabric's Data Wrangler in this episode. Ever wondered how Data Wrangler optimizes the data preparation and exploration process in Microsoft Fabric? 🤔 Eren Orbey, a product manager on the Synapse Data Science team, focusing on machine learning in Microsoft Fabric, unveils data preparation and exploration process in Microsoft Fabric, offering unparalleled insights and behind-the-scenes peeks from the heart of Microsoft’s data innovation! ATTENTION all data engineers and data scientists: If you swear by Pandas in your daily work, this episode is a goldmine for you! Understand the synergy between Pandas and Data Wrangler and discover new tips and techniques that can revolutionize your workflow. 🚀

🏞🔑 Key Takeaways and 🔗 links mentioned in the video:

➡️ How to accelerate data prep with Data Wrangler in Microsoft Fabric https://learn.microsoft.com/en-us/fabric/data-science/data-wrangler

➡️ ➡️ To try out Data Wrangler in VS Code, see Data Wrangler in VS Code https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler

Join us in this insightful journey and unlock the secrets of efficient data storage in Fabric. Be sure to LIKE, COMMENT, and SUBSCRIBE to stay in the loop with our series. Don't forget to hit the notification bell 🔔 to get updates on our latest videos.

Meet the Speakers 🚀 🚀 🚀:

Estera Kot: Senior Product Manager at Microsoft

LinkedIn: https://www.linkedin.com/in/esterakot/

Chapters

  1. 0:00 Introduction
  2. 0:52 What is Data Wrangler
  3. 1:53 Demo
  4. 4:16 Environment overview
  5. 11:36 Exporting the cleaned data
  6. 13:55 Integration with VSCode
  7. 14:59 When to use Data Wrangler
  8. 16:02 Resources

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