Reviewer has chosen not to be AnonymousOverall Impression:
AcceptTechnical Quality of the paper:
Clear noveltyData availability:
All used and produced data are FAIR and openly available in established data repositoriesLength of the manuscript:
The length of this manuscript is about right
Summary of paper in a few sentences (summary of changes and improvements for
second round reviews):
This paper discusses data science education, mostly for the non-computer scientists. A direct experience of the author is presented and discussed in detail. Also, the course is analysed with respect to the Bloom taxonomy, and positioned in comparison with data science courses offered by other institutions.
Reasons to accept:
This paper discusses a topic which is of high importance to the whole data science community, i.e., cross-disciplinary data science education. I believe this gives an important contribution to the ongoing process of establishment of data science education.
Reasons to reject:
My biggest concern is about Section 7, which is rather short and could be developed more. I miss a proper comparison between the courses mentioned in that section and the course described in the paper. Moreover, I believe that it should be possible to find out a slightly larger number of courses to compare with.
Also, looking at the whole paper, Section 5 describes the Data Science Research Projects, Section 6 the Course Evaluation and Students' Feedback, so Section 8 (Lessons Learnt and Societal Implications) seems to follow more naturally than Section 7.
Thus, I propose to merge Section 2 and Section 7.
Moreover, despite the author changes, it is still more of an 'experience' rather than a 'position' paper. I suggest the authors to emphasize more their position.