As I understand, Orange questions are within the scope of this site as long as there is a good explanation and it's related in some way to data science. The Orange tag has had 9 questions asked this month and 72 this year so people see value in said questions, most of which have answers and only one was closed this month (probably two in the future). The closed question was "why is there no volcano plot in the new version of Orange" which is really not relevant to the site / low effort question.
About data mining (16 asked this month, 252 this year), the way I see it is that the tag and questions are relevant to the data science site because oftentimes we are facing doubts about the results and one of the main questions is: "Maybe I messed up something when I was gathering the data", or even problems related to the format questions that people see as relevant on this site given the answers given on said questions. General API or scrapping questions shouldn't belong to data science, something like general BeautifulSoup questions or general Scrapy questions.
Open data is mostly for data source requests so data mining and code don't really belong there. I guess questions about the correct use of APIs would be relevant there as well as on Stack Overflow.
Programming questions should follow the same approach in my opinion. Numpy, Pandas, Sklearn, Keras, etc. are the most commonly used libraries in data science and there will be naturally questions related to them. As long as the question is related to a problem in: processing data, training-testing models, visualization of data and models, storing models and data, ensembles, etc. they should be seen as on-topic. This can be exemplified by: keras Sequential CNN for image data reshaping data issues here a question about how code is directly related to a problem in CNNs and reshaping of data (pre-processing). R is mostly used for data science, ML and statistics and not for much more so it's hard to find an unrelated question in R in my opinion.
What I think it is an interesting proposal for expansion in my opinion are questions about doing data science in the cloud, like, in AWS instances or VPSs. My guess is that more and more people will find problems specific to the use of cloud services and still will be related to data science.