查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

109-1 278451 資料與決策 1 3.0 3 ★ 朴維鎮管理外國學生專班
資財所
國際金融科技專班 11 1 ◎IMBA&IMFI&資財所

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
109-1	278451	資料與決策	1	3.0	3	★	朴維鎮	管理外國學生專班資財所國際金融科技專班	11	1	◎IMBA&IMFI&資財所

教學大綱與進度：

教師姓名朴維鎮

Email eugenepark993@gmail.com

最後更新時間 2020-06-08 12:07:31

課程大綱 This course is about using data to make decisions. Recent years have seen an explosion of the amount of data available to everyone, and making sense of that data has become an operational imperative. The tools of statistics are one of the ways we can make sense of the data and use it to make more effective decisions. The aim of this course is to learn how to make decisions and solve problems through efficient decision-making skills and data mining techniques based on data. Specifically in this course we will discuss basic probability theory and its use as a basis for inferential statistics, discuss efficient decision-making techniques, and develop ability in the use of MINITAB for decision-making analysis.

課程進度 Week 1 Introduction to data analysis for decision-making Week 2 Random variables, mean, median, standard deviation, and variance Week 3 Correlation and covariance: MINITAB practice and interpretation Week 4 Hypothesis tests – Part I Week 5 Hypothesis tests – Part II: MINITAB practice and interpretation Week 6 Simple linear regression – Part I Week 7 Simple linear regression – Part II: MINITAB practice and interpretation Week 8 Interim report – Presentation of individual (or team) project Week 9 Interim report – Presentation of individual (or team) project Week 10 Multiple linear regression – Part I Week 11 Multiple linear regression – Part II: MINITAB practice and interpretation Week 12 Classification – Part I: Logistic regression, MINITAB practice and interpretation Week 13 Classification – Part II: k-NN, MINITAB practice and interpretation Week 14 Classification – Part III: SVM, MINITAB practice and interpretation Week 15 Clustering – Part I: Hierarchical clustering, MINITAB practice and interpretation Week 16 Clustering – Part II: Nonhierarchical clustering, MINITAB practice and interpretation Week 17 Final presentation of individual (or team) project Week 18 Final presentation of individual (or team) project

評量方式與標準 Individual projects: 60 % Assignment : 30 % Attendance: 10 %

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
Data analysis and decision making , 4th Ed. by Albright, Winston, and Zappe Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1)

課程諮詢管道

課程對應SDGs指標

備註

教師姓名	朴維鎮
Email	eugenepark993@gmail.com
最後更新時間	2020-06-08 12:07:31
課程大綱	This course is about using data to make decisions. Recent years have seen an explosion of the amount of data available to everyone, and making sense of that data has become an operational imperative. The tools of statistics are one of the ways we can make sense of the data and use it to make more effective decisions. The aim of this course is to learn how to make decisions and solve problems through efficient decision-making skills and data mining techniques based on data. Specifically in this course we will discuss basic probability theory and its use as a basis for inferential statistics, discuss efficient decision-making techniques, and develop ability in the use of MINITAB for decision-making analysis.
課程進度	Week 1 Introduction to data analysis for decision-making Week 2 Random variables, mean, median, standard deviation, and variance Week 3 Correlation and covariance: MINITAB practice and interpretation Week 4 Hypothesis tests – Part I Week 5 Hypothesis tests – Part II: MINITAB practice and interpretation Week 6 Simple linear regression – Part I Week 7 Simple linear regression – Part II: MINITAB practice and interpretation Week 8 Interim report – Presentation of individual (or team) project Week 9 Interim report – Presentation of individual (or team) project Week 10 Multiple linear regression – Part I Week 11 Multiple linear regression – Part II: MINITAB practice and interpretation Week 12 Classification – Part I: Logistic regression, MINITAB practice and interpretation Week 13 Classification – Part II: k-NN, MINITAB practice and interpretation Week 14 Classification – Part III: SVM, MINITAB practice and interpretation Week 15 Clustering – Part I: Hierarchical clustering, MINITAB practice and interpretation Week 16 Clustering – Part II: Nonhierarchical clustering, MINITAB practice and interpretation Week 17 Final presentation of individual (or team) project Week 18 Final presentation of individual (or team) project
評量方式與標準	Individual projects: 60 % Assignment : 30 % Attendance: 10 %
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 Data analysis and decision making , 4th Ed. by Albright, Winston, and Zappe Engineering Statistics (5th Ed., Wiley) by Montgomery, Runger, and Hubele (ISBN: 978-0-324-59433-1)
課程諮詢管道
課程對應SDGs指標
備註