基本素養 Basic Literacy

倫理推論
學生應具備良好倫理道德觀
Ethical Reasoning
Graduate students should be able to identify ethical dilemmas and to determine necessary courses of action.
全球化思維
學生需具掌握國際間資料科學分析方法的更替、及全球商務變化趨勢的能力
Global Vision
Graduate students should possess a global data science perspective and an awareness of the global business.

核心能力 Competence

口頭表達
學生應具備資料分析概念之溝通表達能力
Speaking
Graduate students should be able to appreciate data analysis approaches and to present research findings/ results effectively in speaking and in writing.
寫作能力
學生應具備良好邏輯之寫作能力
Writing
Graduate students should be able to appreciate data analysis approaches and to present research findings/ results effectively in speaking and in writing.
跨領域性之融合與解題
學生應能針對各不同領域面臨的資料分析問題並提出解決方法
Interdiscip. Competence/ Prob. Solving
Graduate students should be able to integrate different functional areas in solving data analysis problems.
批判思考及創新力
學生應具備良好的批判、思考及創新之能力
Critical Thinking/ Innovation
Graduate students should be able to analyze data effectively and to recommend effective statistical methods.
領導能力
學生應具備良好領導能力
Leadership
Graduate students should be able to demonstrate leadership skills as a data analysis team leader.
團隊合作
學生能在群體中具溝通、領導能力,並運用統計方法與其他背景專長者,一同解決問題
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

本課程旨在介紹資料科學的導論,及系統性地教授資料科學的核心基本技術與原理。除了講解資料科學的基本概念外。並配合不同的應用方向,讓學生能實際體驗其相關所需之知識及技術能力。
This course is designed to introduce an introduction to data science and systematically teach the core basic technologies and principles of data science. In addition to explaining the basic concepts of data science. And with different application directions, students can actually experience the relevant knowledge and technical skills required.

課程學習目標 Course Objectives

  • 使同學對資訊科學有基本了解。
  • 培養同學具備資料分析的基本素養。
  • 課程進度 Progress Description

    進度說明 Progress Description
    1Introduction to Data Science
    2Machine Learning Basics 1
    3Machine Learning Basics 2
    4Machine Learning Basics 3
    5Machine Learning Basics 4
    6Deep Learning Basics 1
    7Deep Learning Basics 2
    8Technical Presentation 1
    9Technical Presentation 2
    10Data Science Applications 1
    11Data Science Applications 2
    12Technical Presentation 3
    13Technical Presentation 4
    14Data Science Applications 3
    15Data Science Applications 4
    16Final Presentation 1
    17Final Presentation 2
    18Final Presentation 3
     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.

    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