Data Visualization and Analysis
Spring 2021 Tuesday, Thursday, 8:30-9:45 AM
Instructor: Dr. Ali Sarvghad
firstname.lastname@example.org, CICS 344
Virtual office hours: Monday 2:30 – 3:30 pm. Zoom link
Teaching Assistant: Joshua Levine
Virtual office hours: Friday 1:30-3 pm. Zoom link
Course delivery policy for Spring 2021
Welcome to Data Visualization and Exploration course (590V). Due to the COVID-19 pandemic, the course will be offered remotely. Lectures will be pre-recorded and provided asynchronously, and discussion sessions will be synchronous. We will record and publish discussions for those of you who can not attend the live sessions due to a major time difference. Please see the class schedule for more details about the lectures and discussion topics.
Information visualization is an area of research that helps people analyze and understand data using visualization techniques. The multi-disciplinary area draws from other areas of science, including human-computer interaction, data science, psychology, and art, to develop new visualization methods and understand how (and why) they are effective.
Information visualization methods are applied to data from many different application domains, including:
- Political reporting and forecasting – as seen on TV and in the papers in the election season.
- News reporting – look at the interactive visualizations used by the New York Times, Wall Street Journal, Slate, etc.
- Social science and economic data, such as census and other surveys, and micro and macroeconomic trends.
- Social networking and web traffic to understand patterns of communication
- Business intelligence and business dashboards – to forecast sales trends, understand competitive marketplace positions, allocate resources, manage production, and logistics.
- Text analysis – to determine trends and relationships for literary analysis and information retrieval.
- Criminal investigations – to portray the relationships between events, people, places, and things.
- Performance analysis of computer networks and systems.
- Software engineering – developing, debugging, and maintaining software.
- Bioinformatics, to understand DNA, gene expressions, systems biology.
- Learn the principles involved in information visualization
- Understand the wide variety of information visualizations and know what visualizations are appropriate for various types of data and for different goals
- Develop skills in critiquing different visualization techniques in the context of user goals and objectives
- Learn how to implement compelling information visualizations
The following textbooks are strongly recommended for this course. Particularly, we will closely follow Tamara Munzner’s book:
- Visualization Analysis and Design, Tamara Munzner, CRC Press, ISBN 9781466508910. Principles and paradigms of visualization design.
- Interactive Data Visualization for the Web, Scott Murray, O’Reilly Media, ISBN 9781449339739. All about D3, the programming tool we will be using for homework and projects.
Grading will be based on the project deliverables, midterm exam, class participation, and final project demo. Final course grades may be curved (but not always). Grading weights are:
|Homework & Discussion||20%|
|Course project & deliverables||45%|
Project details can be found under the Project tab.