AMS 313, Communicating with data

Catalog Description:  This course explores the principles and practices of effective data communication, emphasizing clarity, accuracy, and audience-tailored design.  Students will gain hands-on experience with commom visualization software while learning to critique and select appropriate visualization techniques for different datasets.  The course will also focus on applying ethical standards in data representation and on storytelling techniques to present data in coherent narratives.  By the end of the course, students will be equipped to create impactful visualizations and to communicate data clearly and responsibly in professional settings.

Prerequisite:  AMS Major; AMS 310 

INITIAL OFFERING:  Spring 2026

3 credits
A,B,C grading

SBC:  SPK, WRTD

Course does NOT have the GPNC option.   

REQUIRED Textbook:  None 

RECOMMENDED Textbooks:

1. "How Charts Lie" by Alberto Cairo (https://www.amazon.com/dp/0393358429/)
2. "Fundamentals of Data Visualization" by Claus O. Wilke (https://www.amazon.com/Fundamentals-Data-Visualization-Informative-Compelling/dp/1492031089/ref=sr_1_1). Please also note to the students that there is a free online version of this book at https://clauswilke.com/dataviz/. Only the print version costs money (and is worth it!).
3. "Calling Bullshit" by Carl T. Bergstrom and Jevin D. West (https://www.amazon.com/Calling-Bullshit-Skepticism-Data-Driven-World/dp/0525509208/ref=sr_1_1)

 

Learning Outcomes:

1. Articulate principles of effective data communication: Students will practice the key principles of data communication, including clarity, accuracy, and simplicity, to improve effectiveness.
2. Critique different types of data visualizations: Students will evaluate various types of visualizations (e.g., bar charts, scatter plots) and determine which are most effective or situationally appropriate.
3. Utilize common software: Students will become proficient in using standard data processing and visualization software (such as Tableau, Microsoft Excel, or Python/Matplotlib) to create compelling visualizations.
4. Apply storytelling techniques in data communication: Students will practice structuring communications narratively, thereby guiding the audience from data to insight.
5. Identify and discuss professional standards: Students will critically examine ethical considerations in data communication, including potential biases and misrepresentations, to ensure responsible communication.