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005			20251125110901.0
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007			ta
008			240530t20232023my a|||fs|||| 000 0 eng d
020			_aTHE0009863 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aPSM .H37 2023 r Bc
100	1		_aNorhashimah Binti Mohamed Isa, _eauthor.
245	1	0	_aWork-based-learning company’s preferences for data Analytics students by using analytic hierarchy process (AHP) / _cNorhashimah Binti Mohamed Isa
264		1	_aKuantan, Pahang: _bUMPSA, _c2023
264		4	_c©2023
300			_axiii, 78 pages : _bIllustration (some colour) ; _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCenter for Mathematical Sciences
502			_aBachelor of Applied Science in Data Analytics with Honours--Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical reference
520	3		_aThe 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
610	2	0	_aCenter for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Project _xDissertations
942			_2lcc _cPSM