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Course title |
Information Visualization |
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Semester |
108-1 |
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Designated for |
COLLEGE OF LIBERAL ARTS DEPARTMENT OF LIBRARY AND INFORMATION SCIENCE |
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Instructor |
TIEN-I TSAI |
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Curriculum Number |
LIS5079 |
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Curriculum Identity Number |
126EU1440 |
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Class |
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Credits |
3.0 |
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Full/Half
Yr. |
Half |
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Required/
Elective |
Elective |
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Time |
Tuesday 2,3,4(9:10~12:10) |
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Remarks |
The upper limit of the number of students: 30.
The upper limit of the number of non-majors: 5. |
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Ceiba Web Server |
http://ceiba.ntu.edu.tw/1081LIS5079 |
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Course introduction video |
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Table of Core Capabilities and Curriculum Planning |
Table of Core Capabilities and Curriculum Planning |
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Course Syllabus
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Please respect the intellectual property rights of others and do not copy any of the course information without permission
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Course Description |
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.
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Course Objective |
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.
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Course Requirement |
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 |
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Student Workload (expected study time outside of class per week) |
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Office Hours |
Appointment required. |
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Designated reading |
See course schedule |
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References |
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. |
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Grading |
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Week |
Date |
Topic |
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Week 1 |
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Course Overview |
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Week 2 |
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Introduction to Information Visualization: Overview, History, Relation to Other Disciplines |
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Week 3 |
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Big Data, Visualization, and Digital Humanities |
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Week 4 |
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Visualization Design Principles |
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Week 5 |
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Cleaning Data and Preparing for Visualization |
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Week 6 |
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Visualization Systems and Tools |
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Week 7 |
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Data Analysis and Table/Graph Design |
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Week 8 |
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Networks Visualization |
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Week 9 |
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Midterm project presentations |
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Week 10 |
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Debrief from midterm |
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Week 11 |
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Hierarchies and Trees Visualization |
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Week 12 |
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Temporal and multidimensional data displays |
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Week 13 |
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Geographic Data Visualization |
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Week 14 |
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Large Image Collections and Semantic Data Visualization |
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Week 15 |
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Interaction Techniques and Distortion Current Trends in Information Visualization |
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Week 16 |
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Final project presentations |
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Week 17 |
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No class (New Year Day) |
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Week 18 |
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Final project presentations |
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