INFO 246-13
Information Technology Tools and Applications 
Advanced Topic: Information Visualization
Spring 2020 Syllabus

Dr. Michelle Chen
Other contact information: Zoom Chat
Office Hours: 
Virtually, by appointment via e-mail. Zoom optional drop-in office hours will also be held as needed. More details TBA on the Canvas course website.

Syllabus Links
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Canvas Information: Courses will be available beginning January 23rd, 6 a.m. Pacific Time unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.

You will be enrolled in the Canvas site automatically.

Course Description

Current information professionals are faced with an overwhelming amount of information every day. The information is typically unstructured, abstract, large-scale, and needs a more efficient and intuitive way to represent the relationships, reveal the patterns, and/or discover potential opportunities. Information visualization has thus recently gained increasing attention and begun to be widely applied to scientific, engineering, and social disciplines to help people understand and present their information better. According to Gershon et al. (1998), visualization can provide "an interface between two powerful information processing systems - the human mind and the modern computer."

This course focuses on the state of the art in the field of information visualization. Topics include:

  1. The background of information visualization;
  2. Perceptual and design principles of information visualization;
  3. Data analysis methods and hands-on applications of visualization techniques;
  4. Interaction and interface design issues; and
  5. Exciting emerging trends applied to library and information science fields such as social visualization and visual analytics.

The ultimate goal of this course is to provide technical and non-technical students with an alternative powerful tool to process information in the specific domain of their own interests. 

Course Requirements


  • Discussions (10%, supports CLO#1, CLO#2, CLO#3, CLO#4)
    Students are required to actively participate in class and make thoughtful contributions to class discussions posted on the course website. Students will be evaluated for the involvement in, and intellectual contribution to, the collaborative learning environment.
  • Homework Assignments (60%, supports CLO#1CLO#2CLO#3)
    Four individual assignments will be given throughout the semester to help students review and reinforce what they have learned in class. Assignments contain a mixture of written and hands-on practices.
  • Review Quizzes (5%, supports CLO#1CLO#2CLO#3)
    The two quizzes serve as a review of material in the course lectures and readings with a focus on theoretical concepts; they are open-book, untimed over several days, and all questions may be viewed at once. Each quiz covers approximately one-half of the course content.
  • Semester Project (25%, supports CLO#4)
    Students are expected to work individually or in groups (TBD in week 4) on a semester project and deliver a mini-report (5%), a final project report (10%), and a final project demo (10%). Students will have two options for the semester project (more details TBA on the Canvas course website):
    • Practice visualization techniques with an interesting data set in a particular setting and present the results and critiques; or
    • Relate what we learned in class to students' own professions and present how visualization can be used to enhance their data analysis, discovery, interpretation, and communication processes.

Course Calendar (tentative; subject to minor changes with fair notice)

Weeks Topics
Week 1
Jan 23-26
Introduction to Information Visualization
Introduction Due Jan 26
Week 2
Jan 27-Feb 2
Basic Data Analysis and Graph Design
Week 3
Feb 3-9
Color Theory and Human Perception
Homework #1 Due Feb 9
Week 4
Feb 10-16
Multivariate Visual Representations
Week 5
Feb 17-23

Visual Design Principles
Discussion #1 Due Feb 23

Week 6
Feb 24-Mar 1
Visualization Software and Tools
Homework #2 Due Mar 1
Week 7
Mar 2-8
Worktime Week
Week 8
Mar 9-15
Storytelling with Visualization
Mini Report Due Mar 15
Week 9
Mar 16-22
Interactive Visualization
Quiz 1 Due Mar 22
Homework #3 Part I Due Mar 22
Week 10
Mar 23-29
Critique and Evaluation
Discussion #2 Due Mar 29
Week 11
Mar 30-Apr 5

Spring Recess

Week 12
Apr 6-12

Graph and Network Visualization
Homework #3 Part II Due Apr 12

Week 13
Apr 13-19
Hierarchy and Tree Visualization
Week 14
Apr 20-26
Time Series Visualization
Homework #4 Due Apr 26
Week 15
Apr 27-May 3
Visual Analytics
Discussion #3 Due May 3
Quiz 2 Due May 3
Week 16
May 4-11
Course Wrap-up
Project Demo and Report Due May 11


Deliverables Points (Total = 100)
Discussions (10%) Introduction: 1
Discussion #1: 3
Discussion #2: 3
Discussion #3: 3 
Homework Assignments (60%) Homework #1: 15
Homework #2: 15
Homework #3 (Part I & Part II): 25
Homework #4: 5
Review Quizzes (5%) Quiz #1: 2.5
Quiz #2: 2.5
Semester Project (25%)

Mini Report: 5
Final Project Demo: 10
Final Project Report: 10 

All assignments must be submitted by 11:59 p.m. (Pacific Time) on the day the assignment is due. Late assignments will be reduced by 20% of the total point value per day late. Please contact Dr. Chen if a medical or a family/personal emergency prevents you from submitting an assignment on time. Details of the discussion topics, assignments, review quizzes, and semester project will be given on the Canvas course website.

Course Workload Expectations

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Prerequisites

INFO 246 has no prequisite requirements.

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

  1. Describe the perceptual and cognitive principles of information visualization.
  2. Use data analysis methods and visualization tools to manage and analyze large collections of abstract information.
  3. Identify interaction and interface design issues in visualization.
  4. Apply visualization techniques to specific domains of their own interests for knowledge discovery and retrieval.

Core Competencies (Program Learning Outcomes)

INFO 246 supports the following core competencies:

  1. E Design, query, and evaluate information retrieval systems.
  2. H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.


Required Textbooks:

  • Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press. Available through Amazon: 0970601980arrow gif indicating link outside sjsu domain

Grading Scale

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100 A
94 to 96 A minus
91 to 93 B plus
88 to 90 B
85 to 87 B minus
82 to 84 C plus
79 to 81 C
76 to 78 C minus
73 to 75 D plus
70 to 72 D
67 to 69 D minus
Below 67 F


In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

  • C represents Adequate work; a grade of "C" counts for credit for the course;
  • B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
    For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  • A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).

University Policies

Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: Make sure to visit this page, review and be familiar with these university policies and resources.

In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.

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