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.

[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

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

🔗: https://stanford.edu/~shervine/teaching/cme-106/


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Statistical distributions cheatsheet

Accelerate Data Science Workflows with Zero Code Changes
by nvidia

Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.
By participating in this course, you will:
Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks
Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes
Experience the significant reduction in processing time when workflows are GPU-accelerated

Prerequisites:
Basic understanding of data processing and knowledge of a standard data science workflow on tabular data
Experience using common Python libraries for data analytics
Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX


🆓 Free Online Course
⏰ Duration : More than 1 hour
🏃‍♂️ Self paced
✅ Certification available

Course Link


#datascience #nvidia 
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Completely unimportant but an interesting fact
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