4 ms·
This is the real issue. Getting data is always expensive in any field so it's much easier to analyse data that already exists as you don't need a large grant a
by alexgmcm 8y ago
This is the real issue.
Getting data is always expensive in any field so it's much easier to analyse data that already exists as you don't need a large grant application.
Furthermore, I think the use of certain ML techniques may be akin to resume-driven development particularly for PhD students given that the career prospects in Data Science in industry (and using the HR buzzwords like AI, ML, Deep Learning etc.) are much better than the thin pickings that remain in academia in many fields.
- analog31 8y agoI don't blame the students too much. If you get into a PhD program, it's very hard to change fields or even specialties without starting over. A simpler tactic is to weave a field of interest into your existing research project, then market yourself in that field when you graduate. I did the same thing as a physics student, but with electronics and programming, which furnished me with a marketable resume.