It's very good to see the community is growing, and we're already through the private beta. Since this session ended, I've seen many questions asking for opinion and recommendation, but I was not sure whether or not they were off-topic.
The point is, datascience is not stackoverflow, where a bug is a bug regardless of who's looking at it, or what is the purpose of the application. And since everyone has their own source of information, some questions asking for source-sharing may be on topic too.
The following questions left me asking myself whether I should vote to close or not. Most of them I didn't, both for others in the community had upvoted the question/answers, and for being not sure whether they were actually off-topic.
- Python vs R for machine learning
- Books about the "Science" in Data Science?
- What are some easy to learn machine-learning applications?
- Do I need to learn Hadoop to be a Data Scientist?
- Is Data Science just a trend or is a long term concept?
- What open-source books (or other materials) provide a relatively thorough overview of data science?
- How can I do simple machine learning without hard-coding behavior?
So, my question is, what should be the borderline between on/off-topic recommendation/opinion-based questions?