課程大綱

課程資訊

課程名稱

資訊視覺化
Information Visualization

開課學期

108-1

授課對象

學程知識管理學程

授課教師

蔡天怡

課號

LIS5079

課程識別碼

126EU1440

班次

學分

3.0

全/半年

半年

必/選修

選修

上課時間

星期二2,3,4(9:10~12:10)

上課地點

圖資資訊室

備註

本課程以英語授課。知識管理學程資源領域選修課程。兼通識A6*。
總人數上限：30人
外系人數限制：5人

Ceiba 課程網頁

http://ceiba.ntu.edu.tw/1081LIS5079

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課程概述

Information can be abstract and needs to be processed so that messages are converted to things that make sense to the receivers. Utilizing various digital tools to visualize information helps us deliver information to our target audience in an intuitive and efficient way.
This course provides an overview about state of the art in information visualization. The course highlights the principles of producing effective visualizations and introduces practical visualization procedures, including how to visualize information with software and digital tools such as the Tableau, PlotDB, and Google fusion tables.
Specific topics include:
1. The history and background of information visualization;
2. Design principles of information visualization;
3. Data analysis methods and hands-on applications of visualization techniques;
4. Interface design issues in information visualization;
5. Future trends in information visualization.
The course will be delivered through a combination of lectures, presentations, class activities, and discussions.

課程目標

This course aims to provide students with knowledge of how to effectively visualize information and hands-on experience in visualizing different types of information. The ultimate goal of this course is to provide non-technical students with tools to process, visualize, and analyze information of their own interests (e.g., data collected for their theses).
Upon successful completion of the course, students will be able to:
1. Describe the principles of information visualization;
2. Use data analysis methods and visualization tools, such as Tableau, to manage and analyze abstract information;
3. Identify interface design issues in visualization;
4. Apply visualization techniques to specific domains of their own interests.

課程要求

Students are expected to do weekly readings, to participate in class, and to work in groups for projects. Specific course requirements include:
1. Weekly readings
2. Participation and in-class activities
3. Midterm Project
4. Final Project

預期每週課後學習時數

Office Hours

另約時間

指定閱讀

See course schedule

參考書目

Books and Articles
Börner, K., & Polley, D. E. (2014). Visual insights: a practical guide to
making sense of data. Cambridge, Massachusetts: MIT Press.
Few, S. (2009). Now you see it: Simple visualization techniques for
quantitative analysis. Oakland, CA: Analytics Press.
Few, S. (2012). Show me the numbers: Designing tables and graphs to enlighten
(2nd ed.). Burlingame, Calif.: Analytics Press.
Fry, B. J. (2004). Computational information design. MIT.
Intel IT Center (2013). Big data visualization: Turning big data into big
insights. Intel white paper.
Knaflic, C. N. (2015). Storytelling with data: a data visualization guide for
business professionals. Hoboken, NJ: John Wiley and Sons.
Lankow, J. (2012). Infographics: The Power of Visual Storytelling. Hoboken, NJ:
Wiley.
Magnuson, L. (2016). Data visualization: A guide to visual storytelling for
libraries. Lanham: Rowman & Littlefield.
Mazza, R. (2009). Introduction to information visualization. London: Springer.
Spence, R. (2014). Information visualization: Design for interaction (3rd ed.).
New York: Springer.
Tufte, E. R. (1997). Visual explanations: images and quantities, evidence and
narrative. Cheshire, Conn.: Graphics Press.
Tufte, E. R. (2001). The visual display of quantitative information. Cheshire,
Conn.: Graphics Press.
Tufte, E. R. (2006). Beautiful evidence. Cheshire, Conn.: Graphics Press.
Ware, C. (2013). Information visualization (3rd ed.). Waltham, MA: Morgan
Kaufmann.

Online Resources
Dataviz website: http://www.improving-visualisation.org/case-studies
Google Charts website: https://developers.google.com/chart/
Lee, M. Data Visualization. Retrieved from http://muyueh.com/seeall/
PlotDB website: https://plotdb.com/
R project website: http://www.r-project.org/
R Tutorial. Retrieved from http://cyclismo.org/tutorial/R/ and
http://www.statmethods.net/graphs/index.html
Stefaner, M. Visual tools for the social semantic web. Retrieved from
http://well-formed-data.net/thesis
Tableau Free Training Videos: https://www.tableau.com/learn/training
Tableau Gallery: https://public.tableau.com/s/gallery
Tableau Whitepapers: https://www.tableau.com/learn/whitepapers
Visual methods: information visualization design for the people. Retrieved from
http://visualmethods.blogspot.tw/

Note: Tableau's data
visualization software is provided through the Tableau for Teaching
program.

評量方式
(僅供參考)

No.	項目	百分比	說明
1.	Participation and class activities/assignments	30%
2.	Mid-term project (group)	30%
3.	Final project (group)	40%

課程進度

週次	日期	單元主題
第1週	09/09	Course Overview
第2週	09/17	Introduction to Information Visualization: Overview, History, Relation to Other Disciplines
第3週	09/24	Big Data, Visualization, and Digital Humanities
第4週	10/01	Visualization Design Principles
第5週	10/08	Cleaning Data and Preparing for Visualization
第6週	10/15	Visualization Systems and Tools
第7週	10/22	Groupwork and Discussion
第8週	10/29	Data Analysis and Table/Graph Design
第9週	11/05	Midterm project presentations (online)
第10週	11/12	Networks Visualization
第11週	11/19	Hierarchies and Trees Visualization
第12週	11/26	Temporal and multidimensional data displays
第13週	12/03	Geographic Data Visualization
第14週	12/10	Large Image Collections and Semantic Data Visualization
第15週	12/17	Interaction Techniques and Distortion Current Trends in Information Visualization
第16週	12/24	Final project presentations
第17週	12/31	Final project presentations
第18週	01/07	Final project presentations