I guided a friend through his career transition to become an ML engineer.
Teaching teaches me a lot.
I recently had the opportunity to mentor a friend who has worked as a software engineer (SE) for two years and wants to transition into a data science (DS) role. What started as a casual conversation ended up being several hours of explaining plans to become a data scientist.
His first question was, “What should I learn new?“
Yes, I can write a dozen things in a minute, but it requires a lot more than a list of skills and links to popular courses. FYI, I never answered this question, even in this post.
Some of his existing skills are very important to his new job. He can speed up his transformation by studying one carefully. But the most important thing is to think like a data scientist.
He doesn't have to stop learning anything. However, some of his SE skills are less applicable to data science.
In this post, I summarize some of our discussions. This includes what area of data science suits their interests, what new skills they need to acquire, and how to start small and grow quickly.
