This is not someone misunderstanding correlation for causation, or misinterpreting their independent t-test. In the last year, I’ve also read some really appalling prose trying to explain some simple data analysis, and this was written by data service professionals! not simply be about new ways of interrogating new forms of data, but it should be about bringing writing about data into our domain as well. If data archives manage data, the staff within them should be more aware of the content. We can’t australia rcs data provide the full service to our users unless we are more confident in understanding, manipulating and analysing the data which we hold on behalf of others; and we have to be better at interpreting and analysing the data which we produce ourselves. Typically, I think data services are quite good at this, but we should be better.
This is perhaps a minor skills gap next to the one which we face with big data. There are no fewer than four areas which need consideration. The first surrounds the intersection between traditional statistics and the new forms of data analytics. We can typify one as a branch of mathematics, and the other as a branch of computer science. This rather simplifies the distinction, but both need to understand the provenance and design of the data in order to apply it to ‘real-world’ situations. This has been part of statistics courses for decades, but is only starting to permeate into computer science. How can we make more data manipulators data-curious.