Data Communication - BCB 724

Syllabus

Instructors

Primary faculty

Dr. Michael Love
Department of Genetics
Department of Biostatistics
5009 Genetic Medicine Building (GMB)
Email: love at unc.edu

TA

Brittanie Winfield
BCB PhD candidate in Linnstaedt lab
Email: brittwin at email.unc.edu

Course description

This course is designed to prepare students to be effective communicators of the results of analyses of biological and biomedical data. Students will learn methods for data assessment and exploratory data analysis (EDA), and how to visualize, write, and talk about data in contexts such as emails, reports, lab meetings, publications, and conference presentations. No technical or statistical background required for enrollment.

Course requirements

To obtain full credit, students must attend 7/9 of classes, complete all reading and homework assignments, and achieve a passing grade overall. If any students believe they may have to miss more that two of the lectures, they must discuss this with the lead instructor in advance.

Grading rubric

  • Reading quizzes: 30%
  • Homework assignments: 60%
  • Cross-evaluations: 10%

Syllabus

This course focuses on topics in visualizing, writing, and speaking about biological and biomedical data. Each class will involve short reading assignments about data communication, with in-class discussions and quizzes on that material. Students will explore datasets provided during the course, and typical homework assignments will be to present a particular data analysis result in a written report. Basic R or python code can be used in these assignments. Students will be assessed on communication quality, whereas other BCB courses such as BCB 720 (which can be taken concurrently) focus on statistical inference. Students will evaluate the written products (homework) of other students, to develop their critical mindset for technical diagrams and writing.

Generative AI in BCB 724

Allowed:

  • It is allowed to use Generative AI to correct spelling or grammar in your assignments in BCB 724.

Not allowed:

  • It is not allowed to use Generative AI to write entire paragraphs of text and submit as one’s own work as a writing assigment.
  • It is not allowed to use Generative AI to critique others’ work.

Instructors:

  • Instructors (myself and TAs) will not use Generative AI to grade your assignments.

The point of the class is to help students develop communication skills, to draw upon in a variety of contexts, including collaborative meetings and conferences. In addition, large sections of text from Generative AI is currently somewhat obvious to detect in scientific writing, and including large sections of text that you did not write is likely to go against your own objectives in communication.

Generative AI can be useful in drafting boilerplate communications, or in brainstorming ideas, but I recommend to read the Guidance from the UNC Committee on usage in research, in particular risks regarding bias, confidentiality, and intellectual property/plagiarism.

Note: It is never allowed to paste HIPAA- or FERPA-protected data into public Generative AI tools.

This course is modeled on similar courses, including Dr. Amelia McNamara’s STAT 336 “Data Communication and Visualization” from University of St Thomas, Dr. Karl Broman’s BMI 883 and BMI 884 “Biomedical data science professional skills” from University of Wisconsin-Madison, STATS 404 “Effective Communication in Statistics” from University of Michigan, and Written and Oral Communication in Data Science from Jeffrey Leek, Candace Savonen, Shannon Ellis, and Davon Person at Johns Hopkins University.

Optional textbooks

The class will involved assigned reading of articles/PDFs, but I list some optional books under resources, which we will read sections of during class.