Factors Affecting Students’ Intention Toward Data Science and Analytics Careers: A Comparison Between Two University Programs

Many universities offer new business analytics and data science programs to meet the hot demand for data science and analytics (DSA) professionals. This study uses four data mining methods to investigate students’ intention toward DSA careers and their determinants. The data were collected from students majoring in two DSA programs. It is found that data mining models using the selected variables have better performance than using all variables. When selected variables are used, support vector machine models have a higher accuracy rate and ROC index among the data mining methods. The variable “fits my abilities/potential” is an important predictor of the career intention for both programs. Gender and year of study are found to affect the career intention of business analytics and data science students, respectively. Decision tree models further reveal that business analytics students with a very high perception rating on the variable “related to my program study” are most likely to take up a DSA career. On the other hand, data science students with a high or very high perception rating on the variable “fits my abilities/potential” are most likely to take up a DSA career.