Work-based-learning company’s preferences for data Analytics students by using analytic hierarchy process (AHP) / Norhashimah Binti Mohamed Isa

Center for Mathematical Sciences

Bachelor of Applied Science in Data Analytics with Honours--Universiti Malaysia Pahang – 2023

Includes bibliographical reference

The global economy and society are growing due to the revolution currently undergoing. Job growth in the twenty-first century requires a wide range of talents from those who would like to be trained for the technologies of the day. A lack of knowledge and skills makes it difficult for companies to choose qualified students in Data Analytics for internships. However, this problem is solved by developing the Analytic Hierarchy Process (AHP) model for Work Based-Learning (WBL) in student selection. WBL is a learning approach that combines work experience with collaboration between educational and industrial institutions. It allows students to gain their initial exposure to the real working environment and acquire experience in their preferred field. However, students’ abilities may vary depending on the specific program they are enrolled in. Considering the varying levels of knowledge, experience, and practical training needs among data analytics students, the study reveals that problem-solving skills ranked the highest followed by critical-thinking skills, data visualization skills, programming language, social skills, leadership skills, machine learning, and statistics. Based on companies’ preferences, this indicates the greater importance of soft skills over hard skills. By utilizing this AHP model, companies can optimize their decision-making process in selecting suitable Data Analytics students for Work-Based Learning, thus simplifying the selection process and improving overall efficiency