What topics can I ask about here?
Examples of questions that are on-topic for Data Science Stack Exchange:
- Given process monitoring data arriving every 10ms, what statistical tool should I use to best characterize a change in the process - mean? a distribution?
- I would like to produce a infographic on the 'Brexit' referendum. Given public opinion data across the UK, what are some meaningful techniques to visualize it in a dashboard?
- When executing an ARIMA model in Spark, what are the pros and cons of using Python instead of R?
Data science is a multi-disciplinary field and many new users wonder whether their questions are most appropriate here or on other SE sites, in particular Cross Validated and Stack Overflow. Even though the boundaries are not always perfectly clear and we often accept questions that are also appropriate on other sites, here are a few guiding thoughts:
If you think a question is equally appropriate on multiple sites, ask on the site with the most users (usually Stack Overflow or Cross Validated). That way you have the best chance to get good and quick answers and site contents will stay more coherent. If it is not accepted there, it can be migrated to the correct site. Don't post your questions on more than one site.
Questions are most appropriate here if they are concerned with putting statistical concepts into practice, focus on implementation and (business) processes. Compared with statistics, data science is concerned with implementing whole analytical systems that can ingest (mainly large and diverse) data sets and estimate quantities of interest by incorporating advances from multiple fields.
If you have a programming or implementation question that can be answered without reference to data, it is better suited for Stack Overflow.
If you have a question about the understanding of a machine learning model and its (theoretical) underpinnings, statistical modeling/analysis or probability theory, please refer to Cross Validated.
Other relevant sites include:
- Open Data (Dataset requests)
- AI (Artificial Intelligence concepts and social implications)
- Computational Science (Software packages and algorithms in applied mathematics)