Data Scientists - researchers’ persectives #
“Data scientist” as a term has caught quite some attention as of late (together with all the big data, scalability and
cloud hype). Instead of re-hashing arguments seen in other sources I thought it might make more sense to link to a few
of the thought provoking posts I came across recently.
- In his post Mikio Braun analyses the factors
motivating research in academia vs. engineering in the industry. Nice background material on why work looks so
different in these fields. If you’ve never worked at academia this might be an interesting read to understand why
research groups and research project look so different than your average open source project.
- Some work on why
data science really is just a sub-branch of
statistics.
- An analysis on reasons for hiring a data
scientist, tools and skills of a data
scientist and the common problem of people pretending to be great data scientists.
- Finally a list of skills a data
scientist should have according to Matthew Hurst.
- Also interesting: The
different perspectives of statistics and machine learning