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Graduation requirements for the Master's Program in Statistics consist of 30 credits and a master's thesis. The curriculum and course contents are designed to cultivate interdisciplinary statistics professionals, tailoring coursework to the unique characteristics of various fields to provide rigorous training in both statistical theory and practical applications.

Graduation requirements for the Master's Program in Statistics consist of 30 credits and a master's thesis. The curriculum and course contents are designed to cultivate interdisciplinary statistics professionals, tailoring coursework to the unique characteristics of various fields to provide rigorous training in both statistical theory and practical applications.

The biotechnology and healthcare sectors are key global development priorities. Government agencies and enterprises worldwide—such as the U.S. National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), the U.S. Department of Agriculture (USDA), the European Medicines Agency (EMA), the European Food Safety Authority (EFSA), and Japan’s Ministry of Health, Labour and Welfare—heavily recruit statistical professionals to participate in the development, evaluation, and approval of biotechnological products. The biotech industry is currently undergoing an AI revolution. Through Machine Learning and Deep Learning, healthcare institutions can more accurately predict disease risks, diagnose patient conditions, and develop personalized treatment strategies. Medical databases, such as cancer registries, health insurance databases, and epidemic monitoring systems, have become vital resources for medical research. Leveraging Cloud Computing and Blockchain technology enhances data security and facilitates cross-institutional data sharing, further advancing Precision Public Health. As Taiwan continues to position the biotech industry as a strategic priority for the 21st century, the need for statistical talent to support R&D and health industry advancement is critical. Furthermore, advancements in information technology have led to the maturation of various biomedical databases (such as health insurance and cancer registry databases). Hidden within these massive and messy secondary datasets is critical health information. In the future, professional statisticians will play a pivotal role in data privacy, analytics, and result interpretation, enhancing Taiwan's competitive edge in biotechnology and providing empirical foundations for healthcare and public health policies.

Graduation requirements for the Master's Program in Statistics consist of 30 credits and a master's thesis. The curriculum and course contents are designed to cultivate interdisciplinary statistics professionals, tailoring coursework to the unique characteristics of various fields to provide rigorous training in both statistical theory and practical applications.

Entering the knowledge-based economy, industrial production no longer relies solely on capacity expansion, but rather on statistical quality control and optimization techniques to enhance production efficiency and product quality. With the rise of smart factories and automated production technologies, the industrial sector is actively applying AI and statistical methods to improve efficiency and quality management. The application of statistics in areas such as quality management, process control, and reliability analysis is crucial for the upgrading and transformation of the manufacturing industry. In addition, the challenges brought by global warming and extreme climate change make statistics play a key role in environmental monitoring, resource management, and disaster prevention applications. The frequent occurrence of extreme weather and natural disasters makes the application of statistical analysis and stochastic simulation techniques increasingly important in environmental management and disaster prevention. Located in an earthquake zone and typhoon-affected area with extremely high frequencies of natural disasters, Taiwan experiences a high degree of randomness in meteorological and hydrological variables across different spatiotemporal scales, making statistical methods a core tool for disaster mitigation and earth science research.

Graduation requirements for the Master's Program in Statistics consist of 30 credits and a master's thesis. The curriculum and course contents are designed to cultivate interdisciplinary statistics professionals, tailoring coursework to the unique characteristics of various fields to provide rigorous training in both statistical theory and practical applications.

Over the past decade, the rapid development of the Internet and digital technology has fundamentally transformed social structures and business models. Data collection, analysis, and application have become central to corporate decision-making. Web Analytics has emerged as a specialized field within statistical science, focusing on website traffic analysis, user behavior research, and marketing strategy evaluation. With the rise of Cloud Computing and Big Data Business Applications, corporate decision-making relies heavily on AI and data analysis. International enterprises such as Google have extensively recruited statistical experts to provide data analysis and decision support. Meanwhile, Taiwan's society faces major shifts like declining birth rates and an aging population, which impact not only demographics but also mental health and social policy. Government agencies must rely on real-time and precise data analytics to make appropriate decisions in a rapidly changing social environment. Consequently, the demand for statistical professionals extends beyond the corporate sector to include government and non-profit organizations, focusing on data collection, social surveys, policy evaluation, and large-scale database management.

Graduation requirements for the Master's Program in Statistics consist of 30 credits and a master's thesis. The curriculum and course contents are designed to cultivate interdisciplinary statistics professionals, tailoring coursework to the unique characteristics of various fields to provide rigorous training in both statistical theory and practical applications.

Against the backdrop of the rapid evolution of artificial intelligence, cloud computing, and big data technologies, data has become a shared core asset across all fields. Possessing data processing and statistical analytical skills is no longer confined to a single specialty; rather, it is a critical competency required across all academic disciplines. General elective courses across domains aim to foster cross-disciplinary data literacy, combining theoretical foundations with practical applications to help students from diverse backgrounds understand how data is collected, analyzed, and transformed into knowledge that supports decision-making. Students are guided to engage with modern cloud computing environments and large-scale data processing technologies, understand the analytical challenges and resolution strategies of high-dimensional data in practical settings, and develop hands-on operational and problem-solving skills.

In response to the high demand for data analysis driven by industrial digital transformation, smart decision-making, and public governance, professionals equipped with interdisciplinary statistics and data analysis capabilities have become vital resources jointly valued by businesses and governments. The design of general elective courses across domains aims to break down disciplinary boundaries, enabling students to flexibly apply statistical methods and programming tools to their own specialized fields. Whether in business decision-making, technological R&D, social surveys, or policy evaluation, students can conduct analysis and judgment based on data, thereby enhancing their overall professional competitiveness and social practical application value.