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Skills a Data Scientist Must Have (But a Software Engineer Doesn’t)
I guided a friend through her career transition to being an ML engineer.
Mentoring teaches me a lot.
I recently had the opportunity to guide a friend who worked as a software engineer (SE) for two years and wants to transition to a data science (DS) role. What started as a casual chat eventually became several hours of outlining plans to become a data scientist.
Her first question was, “What should I learn new?”
Of course, I could list a dozen things in a minute, but it requires much more than a list of skills and links to popular courses. FYI, I never answered this question, neither in this post.
Some of her existing skills are invaluable to her new endeavor. She could fast-track her transition by carefully learning one. But what’s more important is thinking like a data scientist.
She doesn’t have to unlearn anything. However, some of her SE skills have little use in data science.
In this post, I summarize some of our discussions. These include which area of data science suits her interests, which new skills she needs to acquire, and how to start small and grow faster.
How do data scientists think differently from software engineers?
This is not to say that one’s work is easier than the others. However, a DS has significantly different goals regarding responsibilities than an SE.
SEs care about designing, developing, and maintaining software. Mostly, SE’s work is more deterministic. In other words, they know the outcome they are building for. And there’s often a finite set of techniques to achieve them.
SEs will have to make many choices in their work and sometimes will have to do course corrections. But these uncertainties will often have predictable solutions.
