I think there's a mismatch between their classification and that of the people surveyed.
The survey characterizes "Scientific development" as "Data analysis + Machine learning", with 28% of the people selecting one of those two latter categories as the answer for "What do you use Python for the most?"
However, only 6% of the users said they were in a company which did science, and only 2% develop for a science industry.
Now, it's certainly true that a scientist can work for a company which neither does science nor targets science research. As an example, an ice cream company may employ food scientists.
It's also possible that people who do, for example, actuarial science might group themselves as working in "insurance" rather than "science".
But it seems wrong to infer that "Scientific development" is equivalent to "Data analysis + Machine learning" without stronger support.
After all, an engineer uses data analysis to evaluate a design, and while engineering is an applied field of science, with a great amount of engineering science to back it up, I don't think many engineers consider themselves as a scientist or as someone doing scientific development.
I think you're expectations of what makes something "scientific development" is a little too high. An analyst using statistical methods - ANOVA, market basket association, association rule learning, outlier detection - is doing ""scientific development". A network engineer working on a congestion algorithm is, too. Postgres? RoR? PHP?
These are all in the realm of scientific development, vis-a-vis non-trivial impacts both socially and commercially.
By "mismatch" I believe that an analyst using statistical methods would typically not consider themself to be a scientist or doing scientific development.
I agree that "science" can be a broad term - I gave the example of engineering as a branch of science. But what use is there to describe 28% of the respondents as doing scientific development if only (say) 6 percent of the people would describe themselves as doing scientific development?
Also, is there nothing else to scientific development besides data analysis or machine learning?
How about this then - if science is a broad term which includes research and development, what sort of software development isn't "scientific development"?
What we have today is the result of scientific development, and software development is among the fields at the very forefront of modern growth. In that context, all of it.
AirBnB? Yup. Uber? Definitely. Facebook? Beyond a doubt. Flickr? Twitter? MySpace? How about open source projects like Apache httpd? Kafka? Linux? What about the guidance software used in the Apollo missions? Think about what the MP3 coding format did for digital media.
It's very easy to take these things for granted, boiled frog and all that.
Then you also disagree with the survey's definition that "Scientific development" = "Data analysis + Machine learning" = 28% of the users.
You agree that there is a mismatch between their classification and yours, and believe it should be 100%.
This supports my argument that their definition is not useful. Rather, it could have been "foobar programmers", and been a more useful as it wouldn't have come with a large amount of existing associations with different meanings.
The survey characterizes "Scientific development" as "Data analysis + Machine learning", with 28% of the people selecting one of those two latter categories as the answer for "What do you use Python for the most?"
However, only 6% of the users said they were in a company which did science, and only 2% develop for a science industry.
Now, it's certainly true that a scientist can work for a company which neither does science nor targets science research. As an example, an ice cream company may employ food scientists.
It's also possible that people who do, for example, actuarial science might group themselves as working in "insurance" rather than "science".
But it seems wrong to infer that "Scientific development" is equivalent to "Data analysis + Machine learning" without stronger support.
After all, an engineer uses data analysis to evaluate a design, and while engineering is an applied field of science, with a great amount of engineering science to back it up, I don't think many engineers consider themselves as a scientist or as someone doing scientific development.