Alumni relations have long been considered key attributes in universities or programs evaluation. However, alumni relations at most universities and programs are narrowed into the evaluation of alumni’s characteristics, lifestyles, types of behavior, and interests, as well as their potential in making contributions, features that are not as helpful to professors and students. In this thesis, I introduce a general programmatic methodology using data science and statistical machine learning techniques to be applied to alumni career path analysis through a relational alumni database. This work aims to bring more insights regarding the career paths of alumni, and in particular, the Mills Computer Science alumni and their careers, and sheds light on the outcomes and expectations of having a Mills computer science education.
|Advisor:||Wang, Susan, Ghofraniha, Jahan|
|Department:||Interdisciplinary Computer Science|
|School Location:||United States -- California|
|Source:||MAI 81/2(E), Masters Abstracts International|
|Subjects:||Computer science, Educational evaluation, Statistics|
|Keywords:||Alumni database, Alumni relations, Career path analysis, Multinomial naive bayes, Program evaluation|
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