That's like saying book A is in Spanish and book B is also in Spanish, so why did you like book B better? Python is just a language, what you do with it is what matters. Sounds like they built something completely distinct from Watson and it did the job.

And that’s fair. I’m always curious with how people are using the same or similar tools that my company uses. It’s good to hear common struggles or differences in workflows.

I suspect it's not that the included Watson Python didn't or wouldn't work but rather that the Watson-Python costs money (via paying for Watson) and Python-Python does not.

Given your perspective, a more important question might be what underlying algorithms are being used? I’m only a rookie data scientist but knowing that (for example) a random forest outperformed a neural net in this case, or even just a set of heuristics, is solid information. These can be built in Python, R, even C depending on the application, developer experience and a bunch of other stuff.

I'm looking at it from a couple of levels to support my data scientists. They're primarily python-based but we are adding newer data scientists who have experience with R. Algorithmically, we built a neural network for phenotyping.

The intern probably had direct access to the data and access to the people with domain knowledge inside the company.

The engineers at IBM were probably separated from the problem by many layers of bureaucracy (meetings, project managers, technical specs).

Even working inside a company, without access to the right data and the right people you aren’t going to get anywhere.

Python written by someone new to the industry and eager to please and make a place for themselves VS Python written by body shop consultants who work at a company famous for failing projects that go well over budget and often simply don't work.