本課程從統計角？如摘要統計、次數分配圖表、比較檢定、相關及迴歸連結至分群、決策樹、類神經網路等資料採礦方法，藉由統計軟體SAS Enterprise Guide (SASEG) & SAS Enterprise Miner(SASEM) 進行巨量資料之採礦分析，培養學生從中發掘暨整合資訊的能？。This course is concerned with data mining, which is the application of the methods of statistics, data analysis and machine learning algorithms to the exploration and analysis of large data sets, with the aim of extracting new, previously unknown, and potentially useful information from the process of knowledge discovery in databases (KDD). Data mining is being applied in an increasing variety of areas, such as financial, economic, high-tech industrial, scientific and medical fields, as an essential component of decision assistance system. Students will learn the ability to analyze massive and complicated data and will be able to turn the raw data into valuable information using the software SAS Enterprise Miner (EM) and Enterprise Guide (EG). The objective of this course is to introduce statistical data mining concepts, describe methods in statistical data mining from sampling to decision trees, and provide decision support solutions.

This course from a statistical perspective? Data mining methods such as summary statistics, frequency distribution charts, comparison tests, correlation and regression links to clustering, decision trees, neural networks and other data mining methods are used to conduct mining analysis of huge amounts of data through the statistical software SAS Enterprise Guide (SASEG) & SAS Enterprise Miner (SASEM), cultivating students' ability to discover and integrate information? . This course is concerned with data mining, which is the application of the methods of statistics, data analysis and machine learning algorithms to the exploration and analysis of large data sets, with the aim of extracting new, previously unknown, and potentially useful information from the process of knowledge discovery in databases (KDD). Data mining is being applied in an increasing variety of areas, such as financial, economic, high-tech industrial, scientific and medical fields, as an essential component of decision assistance system. Students will learn the ability to analyze massive and complicated data and will be able to turn the raw data into valuable information using the software SAS Enterprise Miner (EM) and Enterprise Guide (EG). The objective of this course is to introduce statistical data mining concepts, describe methods in statistical data mining from sampling to decision trees, and provide decision support solutions.

統計資料採礦主要應用統計方法、資料分析和機器學習演算法，進行資料庫之知識挖掘，藉此從高維度大型資料庫中挖掘潛藏的有用資訊。目前已在金融、經濟、行銷、電子商務、數位資訊產業、高科技產業、生命科學和醫學等不同領域，逐漸提升地位成為決策輔助系統中的重要元件之一，提供管理階層決策輔助之用。

Statistical data mining mainly applies statistical methods, data analysis and machine learning algorithms to conduct knowledge mining in databases, thereby mining useful information hidden in high-dimensional large databases. At present, it has gradually improved its status and become one of the important components in decision-making assistance systems in different fields such as finance, economics, marketing, e-commerce, digital information industry, high-tech industry, life sciences and medicine, providing management decision-making assistance.

1.曾淑峰、林志弘、翁玉麟（2012年9月），資料採礦應用—以SAS Enterprise Miner為工具，梅霖文化事業有限公司(ISBN: 978-986-6511-60-8)
2.Slaughter, S.J. and Delwiche, L.D., 蔡宏明、蔡秉諺譯（2011年11月），SAS Enterprise Guide實用工具書，梅霖文化事業有限公司(ISBN: 978-986-6511-58-5)

1. Zeng Shufeng, Lin Zhihong, and Weng Yulin (September 2012), Data Mining Application—using SAS Enterprise Miner as a tool, Meilin Cultural Industry Co., Ltd. (ISBN: 978-986-6511-60-8)
2.Slaughter, S.J. and Delwiche, L.D., translated by Cai Hongming and Cai Bingyan (November 2011), SAS Enterprise Guide practical reference book, Meilin Cultural Enterprise Co., Ltd. (ISBN: 978-986-6511-58-5)