“All scientists are the same, no matter their field.” OK that sounds like a good ‘quotable’ quote, and since I didn’t see it said by anyone else, I can claim it as my own saying. The closest quote to this I saw was “No matter what engineering field you’re in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it.” by Noam Chomsky. These statements are similar but not quite the same.

While the former focuses on what scientists actually DO, the later has more to do with what people LEARN in the process of becoming engineers. The aim of this post is to try to prove that scientists and engineers are essentially the same in terms of the methods, processes and procedures they use to get their job done, no matter their field of endeavour.

To make clearer the argument I’m trying to put up, I am narrowing down my comparison to two ‘types’ of scientists. The first I’d call ‘mainstream scientists’ and the second are data scientists. Who are mainstream scientists? Think of them as the sort of physicists, mathematicians, scientists and engineers that worked on the Orion project. (If you haven’t heard of this project, please watch this video of what the Orion project was about).

So Project Orion was about man trying to ascend to Mars by an atomic bomb propelled by nuclear reactions. Just watching the video and thinking about the scientific process followed, the ‘trial-and-error’ methodology (sic) and the overall project got me thinking that data scientists are just like that! So let’s get down to the actual similarities.

To start with, every introduction to science usually begins with a description of the scientific ‘method’ which (with a little variation here and there) includes: formulation of a question, hypothesis, prediction, testing, analysis, replication, external review, data recording and sharing. (this version of the scientific process was borrowed here). Compare this with the software development life cycle that a data scientist would normally follow: Requirement gathering and analysis, Design, Implementation or coding, Testing, Deployment, Maintenance (source). It’s not difficult now to see that one process was derived from the other, is it?

Moving on, the short name I’d give to much of the scientific and software development process is ‘trial-and-error’ methodology (name has actually been upgraded to ‘Agile’ methodology). Project Orion’s ‘mainstream’ scientists tried (and failed at) several options for getting the rocket to escape Earth’s gravity. Data scientists try several ways to get their analytics done. In both scenarios, sometimes, an incremental step damages the entire progress made so far, and the question of ‘how do we get back to the last good configuration?’ arises. Data scientists have been having good success in recent times in this regard by using some form of version control system (like Git). How do the mainstream scientists manage theirs? I don’t know about now, but Project Orion didn’t have a provision for that.

So are mainstream scientists and data scientists the same? I’ll say a definite yes since they follow similar methods to get their work or research done. If you’re a data scientist, feel free now to identify with every other scientist in the world. Don’t feel any less a scientist because your work does not overtly affect people’s lives (like displacing people for fear of nuclear contamination, or damaging earth’s landscape as an unexpected by-product of your experiments) as mainstream scientists do. In reality, with the tools you have at your disposal as a data scientist, you have the potential to do more damage than that!

And one other quote of Noam Chomsky’s would be a good way to end this post: “If you’re teaching today what you were teaching five years ago, either the field is dead or you are.” So scientists are forward-thinking people, ever innovative, no matter their field, and that’s what makes them scientists.