Data Science and Machine Learning Integration in the Engineering Curriculum: Unlocking Innovations and Opportunities

In 2025, analytical thinking and innovation skills are expected to be the most critical skillset. Therefore, higher education institutions must equip students with skills in demand in the job market. This study aims to evaluate the integration of data science (DS) and machine learning (ML) into the electronics engineering (ECE) curriculum. The survey questionnaires were used to collect responses from four groups of stakeholders. The responses were then analyzed using the valence aware dictionary and sentiment reasoner (VADER) algorithm and Tikhonov regularization method for sentiment analysis. Categorical values were associated using chi-square and p-value. The Latent Dirichlet Algorithm (LDA) was employed to identify latent topics that arose from the engineers’ answers to the open-ended questions. The results of the study show that there is a high degree of similarity between most of the program education objectives (PEO) and program outcome (PO) of the program with the top ten skills in 2025. Similarly, there is a high association between perceived barriers in integrating data science and machine learning in the curriculum between faculty members with different ranks. Among the data science and machine learning applications, student respondents consider internet of things (IoT) analysis, pattern recognition and classification, and optimization and simulation highly relevant. The VADER algorithm shows that 64.29% of students have positive sentiments, while the Tikhonov method has a positive sentiment of 67.86%. Engineers also showed a positive sentiment with an average score of 87.04 versus 88.89 regularized sentiment score. Further study may consider the integration of DS and ML into the syllabus and determine its significant impact on student outcomes.