Reviewer has chosen not to be AnonymousOverall Impression:
AcceptTechnical Quality of the paper:
Incomplete or inappropriateNovelty:
Clear noveltyData availability:
Not all used and produced data are FAIR and openly available in established data repositories; authors need to fix thisLength of the manuscript:
This manuscript is too long for what it presents and should therefore be considerably shortened (below the general length limit)
Summary of paper in a few sentences:
This position paper describes the experience and lessons learnt from the design and teaching of a post-graduate course to train students from multi-diverse disciplines on data science. This course is held at the ETH Zurich and has been running for three years as a 3-credit course during spring semesters. The authors focus on the novelty and challenges to design a data science course oriented to students from a broad range of different disciplines, a training typically oriented to computer science students. Specifically, the authors discussed 1) the design of the course: cross-disciplinar, practical and research-oriented, and the learning strategies applied, the constructivism (learners' prior knowledge and experience) and the transformative (learners' habits of mind and point of view) learning theories; 2) the educational profile status of the students attending the course every year; 3) experience and lessons learnt from the content design of the course, the research projects and its role as a pedagogical artifacts, and the research project teams; 4) course evaluation and students' feedback; 5) the societal implications; and outline the main conclusions drawn.
Reasons to accept:
I think the paper covers an interesting topic: how to train data science beyond the computer science student. I think this topic is significant to position the important role of data science as a necessary skill for future professionals in an every day more digital world. I really like the point that the authors make about the societal impact on training data science as cross-disciplinary. This knowledge is necessary not only to generate more productive professionals, but to provide proficiency, awarnes and freedom to citizens to better live and make decisions in a digital society. On the other hand, it is easy to read and well-structured.
Reasons to reject:
In my opinion this paper misses some discussions and i have further questions for the authors. In the introduction, i would expect more background, for instance a comparison with other post-grade training courses for data science or disciplines of similar cross-disciplinary characteristics such as Bioinformatics. In page 2, the following claim the authors backed with references: "Given the evident lack of plurality and interest for data scientists in the job market, ..." is very surprising to me, above all with an increasing number of news like  and  that state the contrary. It would be beneficial for the argumentation to discuss it in the context of these opposite analyses. In section 3, they state the lecturers of the course are not cross-disciplinary, although they all have experience in multi-disciplinary research. May this fact, the lack of presence of cross-disciplinary lecturers, have effect on the learning results of the course? Do they have plans to evaluate that?. I am also wondering if there is a reason why the course schedules during spring semester. In table 1, figures suggest an increasing lose of interest by life sciences students, could you comment on it? Is the presented course following the Bologna process ? Regarding some observations made by students about course evaluation in section 6, do the authors think that setting up IT technologies such as online forums, could improve and foster communication among students and tutors, and transmission of background knowledge and skills? Regarding lessons learnt exposed in section 7, in line to cultivate critical thinking and constructive doubt, what the authors think about to include an evaluation made by counterpart students on each research project as a part of the final course evaluation? and finally, do they plans to evaluate the real impact of the course in the job market?
On the other hand, there are some other presentation issues that could be poulish: the paper is too long, repetitive sometimes (e.g. page 2, introduction explains twice what this paper is about). Some parts seem to be written in a hurry with some typos, and lists of references do not have a consistent logical order. Some suggestions:
- p2: typo 'learnign'
- p2: typo 'costructivism'
- p4: 'they concern state-of-the-art' --> 'they concern on state-of-the-art'
- Tables 1 and 2: what does 'n' denotes?
- p5: redundant sentence: 'Sections 4-6 illustrate the content and research projects of the course as well as the course evaluation and students' feedback.'
- p5: 'combining the behavioral and design science research strategies.' - i do not understant the meaning of 'behavioral' in this sentence.
- p7: in 'adjusted in an education context' --> replace by 'educational'
- p7: 'results written and orally' --> 'results in writing and orally'
- p8: 'stands the selection' --> 'stands for the selection'
- p8: 'in their illustrations' --> 'in their presentations'
- p8: 'high-quality illustration' --> 'high-quality presentation'
- p8: guide --> guideline
- p8: in the text: step 5 --> step 6
- p8: in the text: step 6 --> step 7
- p8: in the text: step 7 --> step 8
- p9: revision of verb tenses: 'this project conducted' --> 'this project was conducted'; involves --> involved; the project is --> the project was:
- p9: typo 'ran be' --> 'ran being'
- p10: 'it proved not straightforward' --> ..not to be..
- p10: 'two official evaluation' --> evaluations
- p10: 'the general satisfactions' --> satisfaction
- p10: agenda question 3 'what was ... factors' --> factor
- p11: Table 4 caption: Background --> Educational background
- section 6: could be reduced placing interview answers as Supplementary material.
- p12: i think this sentence should be vice-versa like: 'The feedback suggests that working/lab sessions during the class may motivate further the non-computer scientists to improve their knowledge as well as the computer scientists to practice their skills during the course.'
- p14: 'participating citizen' --> participatory citizen
The access to the interview survey described in section 6 is not FAIR.
Indeed this is a relevant topic for the data science community, how to train data science in regard to the job market needs. I think a paper like this is appropriate as a position paper for a data science journal, and i would like that this boosts and active and necessary debate in the community. However, i would expect a bolder claim on the societal impact of training data science for a broader scope of students. This is already stated in the paper but maybe the authors could highlight their vision on the important position of data science in a digital society.
To sum up, the paper raises a novel topic of important societal impact: education of data science for future professionals, and opens an interesting discussion on training data science for students from any educational background. But the papers suffers from verbosity and some presentation issues. It needs careful reading and polishing.