Quantitative and qualitative analysis of current data science programs from perspective of data science competence groups and framework

Data Science is becoming a field connecting multi-year development in areas such as Big Data and Data Analytics, and also applied domains like Bioengineering. Data Science education programs are rapidly being created on all levels. Usually it happens through reuse or renaming and can result in curricula that lack proper balance of competences, which balance is necessary for future data scientists. Our quantitative analysis of over 300 programs worldwide shows that at least one of the three core data science competence groups is under-represented in the majority of programs. Moreover, general business courses are often suggested to students to cover the domain competence group, which in most cases results in superficial treatment of this competence group. Our further qualitative analysis demonstrates that learning outcomes for most of the courses are usually not defined or defined improperly.