[boron1006]

As someone that did my internship in Data Science, if I were to go back and do it again, I would choose Software Engineering instead.

Don't get me wrong, I really liked my position and my team, and loved what I did everyday. Career-wise though, I would consider Software Engineering better, unless you plan on doing a Masters/PHD right after undergrad.

Data Science is a much younger field than Software Engineering. While there is a ton of room to grow, it also means there aren't good hiring practices in place. Companies are way more conservative about hiring Data Scientists than Software Engineers. There usually aren't the same kinds of "coding challenges" as for engineers. While that sounds like a good thing, it means that companies have to filter out candidates some other way. In most cases, (good) companies filter out candidates by looking only at applicants with a graduate degree or with >3 years of experience. This makes it a very tough field to break into without already having experience.

[khannate]

I am actually planning on doing a PhD right after my undergrad. My issue is with what to do before then.

[laurentl]

If you’re set on the (data science, I presume) PhD, I would suggest a SE internship. The rationale is that you’re going to be deep in DS for a few years so this is a good opportunity to explore another field. And what you learn during the internship (how to write clean code and document it, unit tests, version control, seeing production software) will a) put you in good stead for your PhD which presumably will be code-intensive and b) set you apart from the rest of the data science pack once you graduate. I work with (junior / intern, to be fair) data scientists and OMG, bashing together a Jupyter notebook != knowing how to program. How to get from a trained model to production code is in my opinion a vastly underdeveloped topic in data science. Having SE experience will definitely help you see how the other half lives.