GS01 1022 Statistical Communication, Consulting, and Collaborative Data Science
- Course Director(s): Wenyi Wang, Ye Zheng
- Semester: Spring
- Frequency: Annually
- Credit Hours: 2
- Grading System: Letter Grade
- Prerequisites: Consent of Instructor
Description
This course is designed to help students build essential statistical communication skills that are often underemphasized in traditional training. It focuses on preparing students to collaborate effectively with researchers from diverse backgrounds by teaching them how to:
- Effectively interview collaborators to understand their research questions and objectives,
- Articulate mutual goals and expectations specific to statistical consulting and interdisciplinary collaboration,
- Define statistical objectives and deliverables that can guide the research process, and
- Provide regular progress updates in a clear and actionable manner.
Through two core components—a consulting clinic and a project-based learning curriculum—students gain hands-on experience in applying statistical and data science knowledge while refining their communication and collaboration skills.
In the consulting clinic, students offer pro bono statistical consulting services to UTHealth/MDA community researchers, working under instructor supervision to apply these skills in real-world settings. The project-based learning component focuses on project scoping, collaborative practices, and reproducible workflows, equipping students with tools to translate research questions into actionable solutions and foster productive interdisciplinary collaborations.
This course will prepare students for professional collaborations in academic and industry settings.
Prerequires: Students are expected to have knowledge in basic Statistics Inference, Probability, and Linear Regression. Prior programming experience in R or Python is required.
Note: Students who are interested in the course but not sure whether they meet the prerequisites can contact the course directors. If the registration number goes above 12, the course directors will make decisions on who to admit to the course.
Priorities will be given to QS program 1st and 2nd year PhD students and those who meet the prerequisites. The directors will provide guidance on preparing the prerequisites, through taking other basic statistical courses in class or through Coursera courses, for those who are interested in taking it in future.
Course Outline Statistical Communication, Consulting, and Collaborative Data Science