Cross-disciplinary Higher Education of Data Science – Beyond the Computer Science Student

Tracking #: 489-1469

	Name	ORCID
	Evangelos Pournaras	https://orcid.org/0000-0003-3900-2057

Authors:

Submission Type:

Position Paper

Abstract:

The majority of economic sectors are transformed by the abundance of data. Smart grids, smart cities, smart health, Industry 4.0 impose to domain experts requirements for data science skills in order to respond to their duties and the challenges of the digital society. Business training or replacing domain experts with computer scientists can be costly, limiting for the diversity in business sectors and can lead to sacrifice of invaluable domain knowledge. This paper illustrates experience and lessons learnt from the design and teaching of a novel cross-disciplinary data science course at a postgraduate level in a top-class university. The course design is approached from the perspectives of the constructivism and transformative learning theory. Students are introduced to a guideline for a group research project they need to deliver, which is used as a pedagogical artifact for students to unfold their data science skills as well as reflect within their team their domain and prior knowledge. In contrast to other related courses, the course content illustrated is designed to be self-contained for students of different discipline. Without assuming certain prior programming skills, students from different discipline are qualified to practice data science with open-source tools at all stages: data manipulation, interactive graphical analysis, plotting, machine learning and big data analytics. Quantitative and qualitative evaluation with interviews outlines invaluable lessons learnt.

Manuscript:

ds-paper-489.zip

Supplementary Files (optional):

ds-supplementary-489-744.pdf

ds-supplementary-489-745.pdf

Previous Version:

Cross-disciplinary Higher Education of Data Science - Beyond the Computer Science Student

Data repository URLs:

none

Date of Submission:

Friday, June 2, 2017

Nanopublication URLs:

RESPONSE TO REVIEWERS

The author would like to thank once more the reviewers for their positive comments that helped to significantly improve the paper quality.

Reviewer: 2

``This manuscript is too long for what it presents and should therefore be considerably shortened (below the general length limit)"

It was the primary priority of the author to address all reviewers' comments. Several of these comments included requests for new material in the paper. This unavoidably increased the paper size. Cutting down the paper size will unavoidably influence the quality. Therefore, in order to preserve the reflection on reviewers' comments, the authors may keep the paper size as it is.

Reviewer: 3

``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."

In order to not further increase the paper size, Section 7 will not be further expanded. The authors shares the opinion that the courses with which the comparison is made reflect the general trend and conclusions outlines already in the paper.

``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."

Given that Section 7 relies on the storyline developed in the earlier section, the author would keep the section in its current position.

``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."

The author made explicit the positioning of this paper in the conclusions.