רקע אקדמי והצלחה בהכשרת מדעי הנתונים: מחקר גישוש במסלול הטכנולוגי לתואר שני במדעי המידע

Many information science (IS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both classic information science competencies as well as core data science competencies among their students. Since data science competencies often associated with mathematical and computational thinking, departmental officials and prospective students often raise concerns regarding the appropriate background students should have in order to succeed in this newly introduced computational content of the program. In order to address these concerns, we report on an exploratory study through which we examined the 2020 student class of Bar-Ilan University’s IS graduate training, focusing on the core computational data science courses (i.e., supervised and unsupervised machine learning). Our study shows that, to the contrary of many of the expressed concerns, students from the Humanities tend to score higher in data science competencies than those from the Social Sciences and better succeed in the training program as a whole. In addition, students’ Bachelors’ average grade acts as a good indicator for both their success in the training program and in the data science part thereof. In addition, we find no evidence to support concerns regarding age or sex. Finally, our study suggests that the computational data science part of students’ training is very much aligned with the rest of their training program.