Educational Objectives

The School is dedicated to cultivating professionals who combine a solid foundation in data science theory, proficiency in artificial intelligence applications, and practical interdisciplinary experience. Emphasizing the integration of data analytics, AI, generative AI, and digital application capabilities, we prepare students with core competencies in problem analysis, data processing, model building, interdisciplinary synthesis, and practical execution to meet the evolving demands of digital transformation and smart industries.

Curriculum Design & Educational Assessment

• Balanced Theory and Practice:

  Our curriculum model integrates rigorous coursework with hands-on application. Learning experiences include classroom lectures, individual and group capstone projects, lab practice, industry-academic collaborations, specialized research, and external internships to foster independent critical thinking, teamwork, and real-world problem-solving skills.

• Comprehensive Learning Assessment:

  Faculty members employ multifaceted assessment methods—including examinations, assignments, active class participation, project reports, practical deliverables, and oral presentations—to comprehensively evaluate students' academic performance and practical capabilities.

Industry-Academic Collaboration & Career Readiness

The School actively promotes industry partnerships and cross-disciplinary applications across various sectors, collaborating with leading enterprises in finance, media, and technology (such as Fubon, Cathay, and udn). Through hands-on capstone projects, industry lectures, data analytics competitions, internships, and joint research initiatives, we enhance students' understanding of real-world industry contexts while increasing their career prospects.

Furthermore, the School serves as an educational and research platform uniting data science, AI, and digital innovation. Through continuing education and professional training programs, we support industry workforce development, fulfilling our University Social Responsibility (USR) and strengthening industry linkages.

Practical Teaching Framework & Capstone Implementation

Practical internships and graduation capstones form the core of our experiential learning framework:

• Senior Capstone Sequence:

  Senior students complete a full-year graduation capstone sequence, synthesizing their knowledge of data science, AI, and interdisciplinary fields acquired over their first three years to produce deliverables of real practical value.

• Dual Mentorship Model:

  Capstone projects integrate industry collaboration mechanisms, featuring co-guidance from corporate experts and academic faculty to walk students through complete problem-solving and implementation lifecycles.

• Quality Assurance & Internship Guidelines:

  To guarantee educational quality and internship effectiveness, the School maintains official Guidelines for Student Learning Outcome Assessment and External Internships, serving as the operational framework for course execution, capstone implementation, and learning outcome evaluation.