Software Curriculum Transformation at the University Level

Dorin M.; Chong M.; Machuca J. · EDUNINE 2020 - 4th IEEE World Engineering Education Conference: The Challenges of Education in Engineering, Computing and Technology without Exclusions: Innovation in the Era of the Industrial Revolution 4.0, Proceedings (2020) · DOI: 10.1109/edunine48860.2020.9149562

Resumen

Many software-related degrees exist, and a diversity of programs makes it difficult for candidates to choose where they wish to study. Selecting the wrong program costs students time, money, and considerable effort. Though several institutions have created curriculum guidelines for data science related programs, an overall consensus on program content does not exist at either the undergraduate or graduate levels. This paper examines the most common course requirements, such as data mining, machine learning, mathematics, software engineering, data analysis, and data visualization. We then compare the requirement analysis against the specifics of data science related programs offered at the Universidad de Lima, the Universidad Pacifico, and the University of St. Thomas. The results show that all three universities have active programs worth consideration and give students a model of what to look for when selecting their programs.

Instituciones: Universidad de Lima; Universidad del Pacífico