{"id":14603,"date":"2024-07-30T14:10:57","date_gmt":"2024-07-30T06:10:57","guid":{"rendered":"\/stat\/?page_id=14603"},"modified":"2025-11-10T08:34:28","modified_gmt":"2025-11-10T00:34:28","slug":"data-science","status":"publish","type":"page","link":"\/stat\/?page_id=14603","title":{"rendered":"Graduate Program in Data Science"},"content":{"rendered":"<p style=\"margin-right: 17.85pt; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><strong><span style=\"color: #0000cc;\"><span style=\"font-size: 20px;\">Master of Science in Data Science<\/span><\/span><\/strong><\/span><\/span><\/p>\n<h4 style=\"margin-right: 17.85pt; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\">The Master of Science in Data Science program equips students with the interdisciplinary skills required for modern, data-driven problem solving. It provides a strong foundation in statistical reasoning, computational techniques, and the integration of knowledge across diverse domains.<\/span><\/span><\/h4>\n<p style=\"margin-right: 17.85pt; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 20px;\"><strong><span style=\"color: #0000cc;\">Degree Requirements\u00a0<\/span><\/strong><\/span><\/span><\/p>\n<p style=\"margin-right: 17.85pt; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">To earn the Master of Science (M.S.) degree in Data Science, students must complete a minimum of 24 credits, including at least 18 credits from the department. Within this framework, coursework must cover the following areas:<\/span><\/span><\/span><\/p>\n<ul>\n<li><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Statistics Field (3 credits): <\/span><\/span><\/span><\/strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Builds a strong foundation in statistical theory and methodology, enabling students to apply rigorous analytical reasoning to data.<\/span><\/span><\/span><\/li>\n<li><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Information Field (6 credits): <\/span><\/span><\/span><\/strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Emphasizes programming, data management, and computational methods essential for working with large-scale and complex datasets.<\/span><\/span><\/span><\/li>\n<li><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Interdisciplinary Field (6 credits):<\/span><\/span><\/span> <\/strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Bridges statistics, computer science, and domain-specific applications, preparing students to apply data science methods to real-world challenges across disciplines.<\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin-right: 17.85pt; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Graduate students are required to take at least three courses per semester (up to a maximum of 15 credit hours). In addition, they must present the findings of a written report in an oral examination and submit a thesis to the Department.<\/span><\/span><\/span><\/p>\n<p style=\"margin-right: 17.85pt; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">By combining technical expertise with applied problem-solving, the program prepares graduates for careers in data science, analytics, and technology, as well as for advanced academic and research opportunities.<\/span><\/span><\/span><\/p>\n<p><a href=\"https:\/\/azquery.tku.edu.tw\/acad\/default.asp?func=eng\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 20px;\"><strong><span style=\"color: #0000cc;\">Course Information<\/span><\/strong><\/span><\/span><\/a><\/p>\n<p><span style=\"color: #0000cc;\"><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\">Required Courses<\/span><\/span><\/strong><\/span><\/p>\n<p><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 16px;\"><span style=\"color: #000099;\">E0644 Database<\/span><\/span><\/span> <span style=\"font-family: 'Times New Roman', Times, serif; color: #0000cc;\"><span style=\"font-size: 16px;\">(3):\u00a0<\/span><\/span><\/strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 16px;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">The database is an organized collection of data stored and accessed electronically from a computer system. This course provides an overview of the current database management systems(DBMS) and SQL. The goals are 1) to get students familiar with how to use a relational database system to solve real problems; 2) how to use noSQL databases.<\/span><\/span><\/span><\/span><\/p>\n<p><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 16px;\"><span style=\"color: #000099;\">M0800\u00a0Business Ethics<\/span><\/span><\/span> <span style=\"font-family: 'Times New Roman', Times, serif; color: #0000cc;\"><span style=\"font-size: 16px;\"> (1):<\/span><\/span><\/strong> <span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">What other issues dose a business should attend to in addition to its \u201cbottom line\u201d? When a business operates globally, should it modify its ethical standard based on local laws and regulations? This course covers such questions and hopes to provide students with some generally accepted guidelines. Students will not only read about relevant theories in business ethics but also discuss various business ethics issues.<\/span><\/span><\/span><\/p>\n<p style=\"text-align: justify;\"><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 16px;\"><span style=\"color: #000099;\">T0095 Seminar I<\/span><\/span><\/span> <span style=\"font-family: 'Times New Roman', Times, serif; color: #0000cc;\"><span style=\"font-size: 16px;\">(1\/1):<\/span><\/span><\/strong> <span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">The aim of this course is to help graduate students to understand the recent developments and results of statistical research in different areas. This course provides opportunities for students to practice the skills of oral presentation. A few invited talks are also given by some scholars in this semester. With the process of reporting and questioning, it is possible for the students to improve their skills in briefing. The invited talks can also increase the statistical knowledge of students. Furthermore, it intends to improve the research ability and quality of students.<\/span><\/span><\/span><\/p>\n<p><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 16px;\"><span style=\"color: #0000cc;\">T0096 Seminar\u00a0II<\/span><\/span><\/span> <\/strong> <strong><span style=\"font-family: 'Times New Roman', Times, serif; color: #000000;\"><span style=\"font-size: 16px;\">(1\/1):\u00a0 <\/span><\/span><\/strong><span style=\"font-family: 'Times New Roman', Times, serif; color: #000000;\"><span style=\"font-size: 16px;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">The aim of this course is to help graduate students to understand the recent developments and results of statistical and data science research in different areas. This course provides opportunities for students to practice the skills of oral presentation. A few invited talks are also given by some scholars in this semester. With the process of reporting and questioning, it is possible for the students to improve their skills in briefing. The invited talks can also increase the statistical knowledge of students. Furthermore, it intends to improve the research ability and quality of students.<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #000000;\"><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\">Elective Courses\u00a0<\/span><\/span><\/strong><\/span><\/p>\n<ul>\n<li><span style=\"color: #008000;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><span style=\"font-family: 'Times New Roman', Times, serif; color: #000000;\"><span style=\"color: #008000;\"><strong>Statistics Field : <\/strong><\/span>Statistical Analysis Methods (3), Applied Multivariate Analysis (3), Time Series Analysis (3), Data Modeling and Applications (3).<\/span><\/span><\/span><\/span><\/span><\/li>\n<li><span style=\"color: #008000;\"><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Information Field: <\/span><\/span><\/span><\/strong><span style=\"font-family: 'Times New Roman', Times, serif; color: #000000;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Parallel Computing (3), Data Structures (3), R Programming (3), Python Programming (3), Java Programming (3), Cloud Computing (3), Data Mining (3).<\/span><\/span><\/span><\/span><\/li>\n<li><span style=\"color: #008000;\"><span style=\"font-family: 'Times New Roman', Times, serif; color: #000000;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><span style=\"font-size: revert; text-align: justify;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"color: #008000;\"><strong>Interdisciplinary Field: <\/strong><\/span>Text Mining and Social Media Analysis (3), High-dimensional Graphical Techniques (3), Data Visualization (3), Machine Learning (3), Special Topics on Big Data Analytics from Social Media (3), Deep Learning (3), Recommender Systems (3), Special Topics in Data Science Applications (3), Quantitative Finance (3), Machine Learning and Biostatistics Applica in Health Data Science (3), Marketing Data Science (3), E-Commerce and Internet Marketing Practice (3), Data Analysis and Predictive Model (3), Financial Software Applications (3), Artificial Intelligence Business Applications (3), Sustainable Finance (3), Machine Learning Applications and Practice (3), Industry Database Applications and Practices (3), AI and Quantitative Trading Simulation (3), Impact Investing (3).<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Master of Science in Data Science The Master of Science in Data Science program equips students with the interdisciplinary skills required for modern, data-driven problem solving. It provides a strong foundation in statistical reasoning, computational techniques, and the integration of knowledge across diverse domains. Degree Requirements\u00a0 To earn the Master of Science (M.S.) degree in [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-detail-4-page-en","meta":{"_uag_custom_page_level_css":"","footnotes":""},"acf":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"\u9ec3\u6dd1\u82ac","author_link":"\/stat\/?author=6"},"uagb_comment_info":0,"uagb_excerpt":"Master of Science in Data Science The Master of Science in Data Science program equips students with the interdisciplinary skills required for modern, data-driven problem solving. It provides a strong foundation in statistical reasoning, computational techniques, and the integration of knowledge across diverse domains. Degree Requirements\u00a0 To earn the Master of Science (M.S.) degree in&hellip;","_links":{"self":[{"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages\/14603"}],"collection":[{"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"\/stat\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"\/stat\/index.php?rest_route=\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"\/stat\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=14603"}],"version-history":[{"count":14,"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages\/14603\/revisions"}],"predecessor-version":[{"id":33235,"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages\/14603\/revisions\/33235"}],"wp:attachment":[{"href":"\/stat\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14603"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}