{"id":14601,"date":"2024-07-30T14:10:57","date_gmt":"2024-07-30T06:10:57","guid":{"rendered":"\/stat\/?page_id=14601"},"modified":"2025-11-10T08:33:53","modified_gmt":"2025-11-10T00:33:53","slug":"applied-statistics","status":"publish","type":"page","link":"\/stat\/?page_id=14601","title":{"rendered":"Graduate Program in Applied Statistics"},"content":{"rendered":"<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;\">Master of Science in Statistics<\/span><\/strong><\/span><\/span><\/p>\n<p><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><span style=\"color: #ff0000;\"><span style=\"color: #000000;\"><span style=\"font-family: Times New Roman,Times,serif;\">The Master of Science in Statistics program is designed to prepare students for both professional practice and advanced research by combining rigorous training in statistical theory with modern computational tools and applied perspectives. Students gain the analytical, methodological, and programming skills essential for addressing today\u2019s complex data challenges.<\/span><\/span><\/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: 20px;\"><strong><span style=\"color: #0000cc;\">Degree Requirements<\/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 Applied Statistics, students must complete at least 24 credits, including a minimum of 18 credits within the Department of Statistics and Data Science. The curriculum is organized into three core 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;\">Statistical Methods (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;\">Foundations in theory, modeling, and inference for analyzing diverse data types.<\/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;\">Statistical Computing (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;\">Training in programming, computational tools, and algorithms for large or 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;\">Statistical Applications (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;\">Practical application of statistical techniques to real-world problems 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;\">In addition, graduate students are required to take at least three courses each semester (with a maximum of 15 credit hours). They must also present the findings of a written report in an oral examination and submit a thesis to the Department.<\/span><\/span><\/span><\/p>\n<p><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">By integrating theoretical foundations, computational expertise, and applied problem-solving, the program equips graduates for successful careers in statistics, data science, and applied research, as well as for doctoral-level study.<\/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><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Required Courses<\/span><\/span><\/span><\/strong><\/p>\n<p><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><span style=\"color: #0000cc;\"><strong>M0303 Statistical Theory (3):<\/strong><\/span> This course focuses on the theoretical statistics. Topics include distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparison of estimators, confidence sets, theory of hypothesis tests, and Bayesian inference.<\/span><\/span><\/span><\/p>\n<p><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><strong><span style=\"color: #0000cc;\">M0800 Business Ethics (1):<\/span> <\/strong> 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><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><strong><span style=\"color: #0000cc;\">T0095 Seminar I (1\/1):<\/span><\/strong>This course is organized to help graduate students to understand the most recent developments in different areas of statistical research by inviting few talks given by scholars in statistics. Students can give an oral presentation on the paper they chose which is highly related to their graduation thesis.<\/span><\/span><\/span><\/p>\n<p style=\"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;\"><strong><span style=\"color: #0000cc;\">T0096 Seminar II (1\/1):<\/span> <\/strong>This course is organized to help graduate students to understand the most recent developments in different areas of statistical research by inviting few talks given by scholars in statistics. Students can give an oral presentation on the paper they chose which is highly related to their graduation thesis.<\/span><\/span><\/span><\/p>\n<p><strong><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Elective Courses<\/span><\/span><\/span><\/strong><\/p>\n<ul>\n<li><strong><span style=\"color: #339966;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Statistical Methods: <\/span><\/span><\/span><\/span><\/strong><span style=\"color: #000000;\"><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\">Design of Experiments (3), Applied Linear Models (3), Multivariate Analysis (3), Categorical Data Analysis (3), Sampling Theory (3), Functional Data Analysis (3), Time Series (3), Spatial Statistics (3), Quality Control (3), Operations Research (3), Nonparametric Regression (3), Quantile Regression (3), Special Topics in Bayesian Analysis (3).<\/span><\/span><\/span><\/span><\/li>\n<li><span style=\"font-family: Times New Roman,Times,serif;\"><span style=\"font-size: 18px;\"><span style=\"font-size: revert; text-align: start;\"><strong><span style=\"color: #339966;\">Statistical Computing: <\/span><\/strong><span style=\"color: #339966;\"><span style=\"color: #000000;\">Statistical Computing (3), Statistical Computing and Simulation (3), Machine Learning (3), Deep Learning (3), Distributed Computing (3), Advanced Software Applications for Big Data (3).<\/span><\/span><\/span><\/span><\/span><\/li>\n<li><span style=\"font-size: revert; 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;\"><strong><span style=\"color: #339966;\">Statistical Applications: <\/span><\/strong><span style=\"color: #339966;\"><span style=\"color: #000000;\">Statistical Consulting (3), Applications of Statistical Methods in Clinical Trials (3), Data Mining (3), Epidemiology (3), Biostatistics (3), Survival Analysis (3), Analysis of Censored Data (3), Reliability Analysis (3), Theory and Applications of Process Capability Indices (3), Financial Big Data Analysis (3), Artificial Intelligence for Business Application (3), Special Topics in Finance (3), Management Decision Analysis (3), Financial Econometrics (3), Survey Sampling Practice (3), Insurance Actuarial Science (3), Applications of R in Financial Econometrics (3), Financial Software Applications (3), Software Applications for Health Data (3), Advanced Applications of Artificial Intelligence in Biomedicine (1).<\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Master of Science in Statistics The Master of Science in Statistics program is designed to prepare students for both professional practice and advanced research by combining rigorous training in statistical theory with modern computational tools and applied perspectives. Students gain the analytical, methodological, and programming skills essential for addressing today\u2019s complex data challenges. Degree Requirements [&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 Statistics The Master of Science in Statistics program is designed to prepare students for both professional practice and advanced research by combining rigorous training in statistical theory with modern computational tools and applied perspectives. Students gain the analytical, methodological, and programming skills essential for addressing today\u2019s complex data challenges. Degree Requirements&hellip;","_links":{"self":[{"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages\/14601"}],"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=14601"}],"version-history":[{"count":16,"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages\/14601\/revisions"}],"predecessor-version":[{"id":33234,"href":"\/stat\/index.php?rest_route=\/wp\/v2\/pages\/14601\/revisions\/33234"}],"wp:attachment":[{"href":"\/stat\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}