So this is a question for those of you in the comments.
I'm finishing up a Ph.D. in engineering (heavy into climate change research, so tons of programming + mathematical + statistical knowledge in addition to combing through TBs of data with R and other languages).
What kinds of problems are frequently present in the data science industry that differs from academic research?
I'm finishing up a Ph.D. in engineering (heavy into climate change research, so tons of programming + mathematical + statistical knowledge in addition to combing through TBs of data with R and other languages).
What kinds of problems are frequently present in the data science industry that differs from academic research?