Type of Credit: Elective
Credit(s)
Number of Students
資訊視覺化的目的在於透過視覺化的方式呈現資料,協助使用者有效地去理解資料的本質與特性。
本課程採用Visualization Analysis and Design(Tamara Munzner 2014)作為教科書,透過問題導向切入資訊視覺化研究,以What角度思考資料抽象,以Why角度考慮任務抽象,轉化資料的領域知識以視覺化呈現。然後以How的角度選擇最合適的表現方式。最後考量演算法的效率問題並介紹驗證視覺化有效性的方法。輔以近期視覺化論文補充案例分析。
作業以javascript實作動態視覺化為主。
期末專題以分組型式實作具代表性的論文。
第一週有簽到的同學且在遞補名單上,才可以印出加簽單加簽
能力項目說明
本課程培養運用與開發視覺化工具呈現和分析資料的人材,課程將介紹各種資料特性以及對應的視覺化演算法。並透過作業與期末專案針對實際資料設計視覺化程式。
教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
---|---|---|
每週教學時數3小時、課前課後學生投入時間3小時
Week 1: Introduction
• What's Vis, and Why Do It?
• Why have a Human/Computer in the Loop?
• Why focus on Tasks/Effectiveness?
課堂講授
Week 2: What: Data abstraction
• Data type, Attribute type, and Semantics
課堂講授、資料收集
Week 3: Why: Task abstraction
• Actions: analyze, produce, search, and query
• Target
課堂講授、題目構思
Week 4: Analysis: Four level design
• Four level design
• Validation approaches
課堂講授、程式上機-視覺分析
Week 5: Marks and Channels
• Definition of Marks and Channels
• Validation approaches
課堂講授、程式上機-Marks
Week 6: Rules of Thumb
• Justification and Alternatives
• Memory and attention
課堂講授、分組提案報告
Week 7: Arrange Tables & Arrange Spatial Data
• Separate, Order, and Align; Spatial Axis Orientation and layout density
• Geometry; Scalar field, Vector field, and Tensor field
課堂講授
Week 8: Arrange Networks and Trees
• Connections
• Matrix view
課堂講授
Week 9: Midterm
期中考試
Week 10: Map Color and Other Channels
• Color theory, Color maps, and Other channels
課堂講授、程式上機
Week 11: Manipulate View
• Change view over time
• Select elements and navigation
課堂講授
Week 12: Facet into Multiple Views
• Juxtapose and coordinate view
• Partition into view
課堂講授
Week 13: 自主學習
Week 14: Reduce Items and Attributes
• Filter and aggregation
課堂講授
Week 15: Embed: Focus+Context
• Focus+Context related paper
課堂講授
Week 16: Analysis Case Studies (1)
• Selected paper
案例介紹
Week 17: Final Project Presentation
• 期末分組報告
Week 18: 自主學習
作業/上課表現 30%
期中考30%
期末專題 40%
教科書
Visualization Analysis and Design. by Tamara Munzner. A K Peters/CRC Press; (December 3, 2014)
參考書目
視覺化資料─100% 全腦吸收大數據,直入神經元。陳為、沈則潛、陶煜波。佳魁資訊。2014
https://www.cs.nccu.edu.tw/~mtchi/course/vis24