140.610.79
Data Visualization
Location
Internet
Term
Summer Institute
Department
Biostatistics
Credit(s)
1
Academic Year
2025 - 2026
Instruction Method
Synchronous Online
Start Date
Tuesday, June 10, 2025
End Date
Thursday, June 12, 2025
Tu, W, Th, 9:00 - 10:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
One Year Only
Resources
Prerequisite
Experience with elementary statistics and linear modeling and basic familiarity with R and RStudio.
Students will learn the foundational principles and practical skills to effectively visualize and interpret data, enhancing their ability to communicate complex statistical findings clearly and persuasively. In fields like epidemiology, biostatistics, and data science, where decisions are often informed by large and complex datasets, visualization is essential.
Explores the principles and practices of effective data visualization. Emphasizes the role of visualization in statistical reasoning, modeling, and communication. Prepares students to create clear, accurate, and impactful visualizations for audiences in both academia and industry. Discusses strategies for conveying complex ideas with data, addressing common pitfalls and challenges. Focuses on examples from epidemiology, public health, and applied data science to demonstrate real-world applications. Practical applications will be taught primarily using R, ggplot2, and supporting libraries. Techniques using Python, Stata, and time series dashboard software will also be addressed.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Conceptualize and implement effective data visualizations using R and ggplot2.
- Select appropriate visualization techniques based on data type, analytical goals, and audience needs.
- Critically evaluate data visualizations for effectiveness and accuracy.
- Communicate complex data insights through clear and impactful storytelling.
- Incorporate reproducibility and transparency into the data visualization process using modern coding practices.
- Address common challenges and pitfalls in data visualization, including choosing incorrect statistical transformations, misleading uses of scale and range, and poor design.
- Integrate visualizations into workflows for statistical modeling and applied data science.
Methods of Assessment
This course is evaluated as follows:
- 20% Participation
- 30% Problem sets
- 50% Final Project