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
評分項目 Grading Method |
配分比例 Percentage |
說明 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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相似課程 Related Courses
課程代碼 Course Code |
課程名稱 Course Name |
授課教師 Instructor |
時間地點 Time & Room |
學分 Credits |
操作 Actions |
---|---|---|---|---|---|
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
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上課時間 Course Time:Monday/6,7,8[M009]
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授課教師 Teacher:金泰星
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修課班級 Class:共選修1-4(管院開)
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選課備註 Memo:全英授課,開放全校學生修習,限30人。
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