Data science/ML/AI

Introduction to Probability and Statistics for Engineers List of probability and statistics cheatsheets by Stanford

[Compilation]1000+ Data Science Interview Questions/Preparation Resources Compilation created by kaggle users 1. GIT interview questions for DS and SQL Interview questions 2. 50 ML questions 3. Four years on interview questions 4. Compilation of pandas interview questions 5. Difference between common ML algortihms 6. Scenario based Data questions 7. Top python interview questions 8. Internship questions for DS interns 9. Questions from DS- Netflix 10. India specific Data science interview questions 11. R interview questions 12. Explain a project in Data science 13. A great collection of cheatsheets, analyzed here 14. A collection of questions on Github here 15. Cheat Sheets for Machine Learning Interview Topics 16. Compiled list of 600+ Q&As for Data Science interview prep 🎉 17. Approaching almost any ML Problem, originally shared on Kaggle 18. A Basics refresher 19. A notebook 20. Companies and Data Science Interview questions Megathread 21. Data Scientist - Interview Question Bank 22. ML Interview questions 23. Machine Learning Interviews Book 👇 https://www.kaggle.com/discussions/questions-and-answers/239533 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

For all Data Engineers out there, here is The State of Data Engineering 2024 Some of the highlights: ✅ More and more, data observability tools are used not just to monitor data sources, but also the infrastructure, pipelines, and systems after data is collected. ✅ Companies are now seeing data observability as essential for their AI projects. Gartner has called it a must-have for AI-ready data. ✅ Like in 2023, Monte Carlo is leading in this area, with G2 naming them the #1 Data Observability Platform. Big organizations like Cisco, American Airlines, and NASDAQ use Monte Carlo to make their AI systems more reliable.