Data Science W209
Communicating clearly and effectively about the patterns we find in data is a key skill for a successful data scientist. This course focuses on the design and implementation of complementary visual and verbal representations of patterns and analyses in order to convey findings, answer questions, drive decisions, and provide persuasive evidence supported by data. Assignments will give hands-on experience designing data graphics and visualizations, and reporting findings in prose.
Visualization enhances exploratory analysis as well as efficient communication of data results. This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. The goal is to give you the practical knowledge you need to create effective tools for both exploring and explaining your data. Exercises throughout the course provide a hands-on experience using relevant programming libraries and software tools to apply research and design concepts learned.
After completing this course, you should be able to
- Constructively critique existing visualizations, identifying issues of integrity as well as excellence.
- Analyze data using exploratory visualization.
- Design aesthetically pleasing static and interactive visualizations with perceptually appropriate forms and encodings.
- Improve your own work through usability testing and iteration, with attention to context.
- Build commonly requested types of visualizations as well as more advanced visualizations using ground-up customization.
- Create useful, performant visualizations from real-world data sources, including large and complex datasets.
- Select appropriate tools for building visualizations, and gain skills to evaluate new tools.
Course must be taken for a letter grade to fulfill degree requirements.