Data Engineering and Data Science Newsletter #5


The goal of this Insight Extractor’s newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

1. Why Most Analytics efforts fail?

Fantastic post from Crystal Widjaja (ex Go-Jek SVP of BI) on why most analytics efforts fail? It walks you through steps by step process that you should follow to ensure that Analytics efforts in your org are successful. Must Read! If there’s one post that you read from this newsletter, then pick this one here

2. Data Engineer vs Data Scientist vs Machine Learning engineer

A good discussion on how do data scientists, data engineers, and machine learning engineers differ and where do they overlap. Youtube Video here

3. Three steps in Data Modeling

Learn about the 3 steps in data modeling (conceptual, logical, and physical) on Youtube here

4. Improving Product Recommendations:

Learn about the advances in product recommendations algorithm through Amazon’s science blog here

5. Top 5 SQL problems to solve

Good list on few problems that you should know how to solve for learning SQL. List here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

Untitled (27).png

What do you think? Leave a comment below.