UC San Diego

Data science is an emerging discipline that has grown increasingly popular in the past decade. In response, numerous schools have developed and launched their own master’s level data science programs. Through the development and launch of these programs, universities are selecting aspects of data science to structure their programs around, which then further develops data science as a discipline. Of the three aspects that compose data science, Theoretical Knowledge, Technical Execution, and Human Oriented Professional Skills (HOPS), universities can choose to value certain aspects over others through their program descriptions, program prerequisites, and mandated courses. This work found that programs tend to gravitate towards favoring technical execution aspects in mandated courses, favoring theoretical knowledge in program prerequisites, while neglecting the aspect of HOPS in all areas of the program. The consequences of this are threefold. First, when theoretical knowledge is used as a program prerequisite it can prevent students from non-STEM backgrounds from entering into data science, reducing the thought diversity in the field. Second, when HOPS are neglected, students cannot effectively access knowledge from other disciplines, closing off data science from new tools, methods, and problems. Third, when HOPS skills are neglected in favor of Technical Execution courses, students learn how to apply tools, but perhaps not judge the consequences or implications of their tool or method choices.