基本素養 Basic Literacy

倫理推論
統計專業者主要從事蒐集資料和資料分析的工作,對資料的使用與保密,須遵循個資法的規範
Ethical Reasoning
Graduate students should be able to identify ethical dilemmas and to determine necessary courses of action
全球化思維
學生需具自我學習的能力,適時的掌握現在國際間統計分析方法的更替,及全球商務變化地趨勢
Global Vision
Graduate students should possess a global statistical perspective and an awareness of the global business

核心能力 Competence

口頭表達能力
學生應具備口頭表達及描述能力,能將統計方法做適當描述、及與人溝通統計想法、概念、使用方式
Speaking
Graduate students should be able to appreciate statistical research and to present research findings/ results effectively in speaking
寫作能力
學生能具備良好寫作能力,能將統計方法做適當描述、及與人溝通統計想法、概念、使用方式
Writing
Graduate students should be able to appreciate statistical research and to present research findings/ results effectively in writing
跨領域性之融合與解題
學生不僅需瞭解統計方法的來龍去脈,還需瞭解問題的本質,才能提出適合的統計方法解決問題
Interdiscip. Competence/ Prob. Solving
Graduate students should be able to integrate different functional areas in solving statistical problems
批判思考及創新力
學生要能就所面對的資料及時提供出可行的統計分析策略及迅速完成相關資料分析
Critical Thinking/ Innovation
Graduate students should be able to analyze data effectively and to recommend effective statistical methods
領導能力
同學需具備領導其他專長同仁解讀數據的才能
Leadership
Graduate students should be able to demonstrate leadership skills of a data analysis manager
團隊合作
學生應具有與其他背景專長者,一同解決職場上問題的能力
Teamwork
Graduate students should be able to coordinate actions and solve problems jointly with other members of a professional team

課程概述 Course Description

‧ Methodologies for handling missing data in statistical analyses. It covers naive methods, missing data assumptions, likelihood-based approaches, imputation approaches, inverse-probability weighting, pattern-mixture models, sensitivity analysis and approaches under nonignorable missingness. ‧ Computational tools such as the Expectation-Maximization algorithm and the Gibbs’ sampler will be intro-duced. This course is intended for students who are interested in methodological research.
‧ Methodologies for handling missing data in statistical analyses. It covers naive methods, missing data assumptions, likelihood-based approaches, imputation approaches, inverse-probability weighting, pattern-mixture models, sensitivity analysis and approaches under nonignorable missingness. ‧ Computational tools such as the Expectation-Maximization algorithm and the Gibbs’ sampler will be intro-duced. This course is intended for students who are interested in methodological research.

課程學習目標 Course Objectives

  • be able to understand the basic theory related to the missing data
  • be able to distinguish the types, patterns and processes of missing data
  • be able to handle missing data appropriately
  • be capable to implement the statistical software to deal with missing data
  • be able to classify or distinguish missing data among coarse data
  • analyze correctly a real-world problem in the presence of missing data framework
  • use their own programs, R, SAS, or Python, for handling the missing data.
  • 課程進度 Progress Description

    進度說明 Progress Description
    1General Introduction
    2General Introduction
    3Simple methods of missing data
    4Simple methods of missing data
    5Likelihood-based Methods under missingness
    6Likelihood-based Methods under missingness
    7Multiple Imputation Methods under missingness
    8Multiple Imputation Methods under missingness
    9Multiple Imputation Methods under missingness
    10Inverse Probability Weighting Methods under missingness
    11Inverse Probability Weighting Methods under missingness
    12Sensitivity Analysis for missing data
    13Sensitivity Analysis for missing data
    14Special Topic 1
    15Special Topic 1
    16Special topic 2
    17Special topic 2
    18Final term project
     以上每週進度教師可依上課情況做適度調整。The schedule may be subject to change.

    課程是否與永續發展目標相關調查
    Survey of the conntent relevant to SDGs

    本課程與SDGs相關項目如下:
    This course is relevant to these items of SDGs as following:
    • 性別平等 (Gender Equality)

    有關課程其他調查 Other Surveys of Courses

    1.本課程是否規劃業界教師參與教學或演講? 否
    Is there any industry specialist invited in this course? How many times? No
    2.本課程是否規劃含校外實習(並非參訪)? 否
    Are there any internships involved in the course? How many hours? No
    3.本課程是否可歸認為學術倫理課程? 否
    Is this course recognized as an academic ethics course? In the course how many hours are regarding academic ethics topics? No
    4.本課程是否屬進入社區實踐課程? 否
    Is this course recognized as a Community engagement and Service learning course? Which community will be engaged? No