We have students from the following departments:
Business/Marketing , Statistics, Earth and Environmental Engineering, Biomedical Informatics, Industrial Engineering/Operations Research, Actuarial Science, Quantitative Methods in the Social Sciences, Psychology, Economics, Physics, Politics, Mechanical Engineering, Sociology, History, Journalism
(Some departments have more than others. If I missed anyone, sorry! You’re welcome too.)

We have students who are Undergrads, Masters students, PhD students , Post-Docs, and Professors

We have students who probably will be on the (non-academic) job market sometime in the next year, and will be interested in getting jobs in start-ups and corporations, government, non-profits…? (I don’t know all your aspirations.) And want to be adequately prepared for those jobs.
We have students who are researchers and trying to deeply understand their academic specialties so they can extend them.
So I discriminate somewhat between:
— Undergrads/Terminal Masters regardless of field who could be thought of as pre-professional [also that reminds me there are people who are in continuing ed/working professional.]
— Researchers
The separation in groups to some extent suggests two goals: (1) Learn best practices and (2) Define Data Science as a research area.
Though I think both groups are interested to varying degrees in both goals. But the two goals are quite different!

It also suggests to me that we all speak different languages. To be addressed in next post.