Information Technology Tools and Applications – Advanced Topic: Information Visualization
Spring 2017 Greensheet
Dr. Michelle Chen
Office Hours: Virtually, by appointment via e-mail or Blackboard IM. Blackboard Collaborate optional drop-in office hours will also be held as needed. More details TBA on the Canvas course website.
Canvas Login and Tutorials
Canvas Information: Courses will be available beginning January 26th, 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 into the Canvas site automatically.
Be sure to logon to the course site no later than Friday, January 27th, to begin the first lesson.
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 - human mind and the modern computer."
This course focuses on the state of the art in the field of information visualization. Topics include:
- The background of information visualization;
- Perceptual and design principles of information visualization;
- Data analysis methods and hands-on applications of visualization techniques;
- Interaction and interface design issues; and
- 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.
- 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#1, CLO#2, CLO#3)
Four individual assignments (equally weighted) 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#1, CLO#2, CLO#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 change with fair notice)
|Introduction to Information Visualization
Introduction Due Jan 29
Jan 30-Feb 5
|Basic Data Analysis and Graph Design|
|Color Theory and Human Perception
Homework #1 Due Feb 12
|Multivariate Visual Representations|
Visual Design Principles
Feb 27-Mar 5
|Visualization Software and Tools
Homework #2 Due Mar 5
|Storytelling with Visualization
Mini Report Due Mar 19
Quiz 1 Due Mar 26
Homework #3 Due Mar 26
Mar 27-Apr 2
|Critique and Evaluation
Discussion #2 Due Apr 9
|Graph and Network Visualization|
|Hierarchy and Tree Visualization
Homework #4 Due Apr 23
|Time Series Visualization|
Discussion #3 Due May 7
Quiz 2 Due May 7
Project Demo and Report Due May 16
|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: 15
Homework #4: 15
|Review Quizzes (5%)||Quiz #1: 2.5
Quiz #2: 2.5
|Semester Project (25%)||
Mini Report: 5
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 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.
INFO 246 has no prerequisite requirements.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Describe the perceptual and cognitive principles of information visualization.
- Use data analysis methods and visualization tools to manage and analyze large collections of abstract information.
- Identify interaction and interface design issues in visualization.
- Apply visualization techniques to specific domains of their own interests for knowledge discovery and retrieval.
INFO 246 supports the following core competencies:
- E Design, query, and evaluate information retrieval systems.
- H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.
- Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA: Analytics Press. Available through Amazon: 0970601980
The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:
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;
For core courses in the MLIS program (not MARA) — 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.
Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA).
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:
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