INFO 5312 - Data Communication

Spring. 3 credits. Student option grading (no audit).

Prerequisite: INFO 5001. Prior experience with R and Git is required. Co-meets with INFO 3312.

B. Soltoff.

Data scientists often present information to disseminate their findings. This course introduces theories and applications of communicating with data, with an emphasis on visualizations. To support this approach, we will focus on the what, why, and how of data visualization. “What” focuses on specific types of visualizations for a particular purpose, as well as tools for constructing these plots. In “how” we will focus on the process of generating a data visualization from pre-processing the raw data, mapping attributes of the data to plot aesthetics, strategically determining how to define the visual encoding of the data for maximal accessibility, and finalizing the visualization to consider the importance of visual appeal. In “why” we discuss the theory tying together the “how” and the “what”, and consider empirical evidence of best-practices in data communication.

Outcome 1: Implement principles of designing and creating effective data visualizations.

Outcome 2: Evaluate, critique, and improve upon one’s own and others’ data visualizations based on how good a job the visualization does for communicating a message clearly and correctly.

Outcome 3: Post-process and refine plots for effective communication.

Outcome 4: Master using R and a variety of modern data visualization packages to reproducibly create data visualizations.