MaDaScA: Instruction of Data Science to Managers

Shahar Golan, Dan Bouhnik
InSITE 2019  •  2019  •  pp. 125-140
Aim/Purpose: Build a program that teaches prospect managers the skills that are relevant for leading data science activity.

Background: Data science becomes ubiquitous in organizations. It is imperative to train students in management departments in the skills that are relevant to this field. Most courses in data science focus on technical knowledge like model building methods, and neglect organizational knowledge such as team roles, ethical considerations and project stages. This work suggests a complementary program that supplies the students with the required knowledge. The authors believe that this program is most suitable for management-students, and that it can also be adapted to software engineering students, in order to provide them with a wider scope.

Contribution: We present the MaDaScA (Managing Data Science Activity) program. The program defines a list of topics that are required for managers’ education in order to lead data science activity. This work suggests the content and take-away messages of each topic. The paper surveys several existing courses that teach data-science to managers.

Findings: All existing courses supply a part of the suggested topics, either focusing on technical aspects of data-science or on organizational aspects. In particular, only a small minority of the courses discuss ethical aspects of data science.

Recommendations for Practitioners: We recommend adopting MaDaScA in management departments in order to prepare managers for the challenges in data-science.

Recommendations for Researchers: We recommend adapting the MaDaScA model to the curriculum of the faculty of engineering, especially for the department of industrial engineering.

Impact on Society: Educating prospect managers on the capabilities of data science and responsibilities that come with it is key for making sure organizations become much more data driven, efficient and ethical.

Future Research: It is possible to make this program more effective by adding practical experience
data-science, data-science instruction, management
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