1173 - 資料視覺化分析 英授 Taught in English

Data Visualization Analysis

教育目標 Course Target

Graphical Data Analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis, data mining, and network analysis. The primary focus of this course is to equip students with the necessary knowledge and skills to utilize computer software, such as Python, R, Jamovi, JASP, and Excel, to analyze and visualize data using graphical displays. By using real datasets, students will learn how graphic displays can reveal hidden patterns and trends in data that are not always apparent through traditional statistical methods. The course will cover a range of topics related to Graphical Data Analysis, including data visualization principles, statistical graphics, exploratory data analysis, and data preparation. By the end of the course, students will have a solid understanding of these concepts and will be able to apply them to a variety of real-world data analysis problems.

Graphical Data Analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploration data analysis, data mining, and network analysis. The primary focus of this course is to equip students with the necessary knowledge and skills to utilize computer software, such as Python, R, Jamovi, JASP, and Excel, to analyze and visualize data using graphic displays. By using real datasets, students will learn how graphic displays can reveal hidden patterns and trends in data that are not always apparent through traditional statistical methods. The course will cover a range of topics related to Graphical Data Analysis, including data visualization principles, statistical graphics, exploration data analysis, and data preparation. By the end of the course, students will have a solid understanding of these concepts and will be able to apply them to a variety of real-world data analysis problems.

參考書目 Reference Books

Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data (2nd ed.). O’Reilly. https://r-graphics.org (Free)

Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data (2nd ed.). O’Reilly. https://r-graphics.org (Free)

評分方式 Grading

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Description
Attendance and class paticipation
Attendance and class participation
30 Students are required to attend class
Assignments
Assignments
30
Final project
Final project
40

授課大綱 Course Plan

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選修-5573
工工碩博1,2 劉士嘉 五/2,3,4[IEⅡ102] 3-0 詳細資訊 Details

課程資訊 Course Information

基本資料 Basic Information

  • 課程代碼 Course Code: 1173
  • 學分 Credit: 3-0
  • 上課時間 Course Time:
    Monday/6,7,8[M009]
  • 授課教師 Teacher:
    金泰星
  • 修課班級 Class:
    共選修1-4(管院開)
  • 選課備註 Memo:
    全英授課,開放全校學生修習,限30人。
選課狀態 Enrollment Status

目前選課人數 Current Enrollment: 14 人

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