“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