Computational Theory and Data Visualization (Fall 2022 TA)

Teaching Assistant

My primary responsibilities were grading homework assignments (17), providing detailed feedback on student work, monitoring discussion board (10 Modules), promptly replying to posts, and helped with WileyPLUS homework. To ensure effective communication and support, I maintained a high level of organization and ensured that the students’ grades were accurate. I made myself readily available to students by responding promptly to emails, offering virtual office hours, and providing assistance both during and outside of scheduled office hours. Additionally, I closely monitored students’ progress throughout the semester and scheduled Zoom meetings with students to address any questions or concerns that arose during the course.

Textbooks

• Required Textbook: none
• Optional Textbooks:
  • Shravan Vasishth and Michael Broe, “The Foundations of Statistics: A Simulation-based Approach”
  • William N. Venables and David M. Smith, “An Introduction to R”

Course Description

This course aims to teach students to use programming to gain intuition about statistical theory and fundamental concepts and to visualize data appropriately. Specifically, computational methods covered include simulation methods and numerical methods in maximization and integration. Appropriate graphical displays of statistical and simulation results will be emphasized. Statistical concepts covered include sampling distributions, confidence intervals and p-values, the central limit theorem, expectation, and maximum likelihood estimation. Student understanding of course ideas will rely heavily on performing simulation studies and discussing the assimilated class results online.

Student Learning Outcomes

The primary goals of this course are:

• Perform basic statistical programming tasks.
• Create appropriate graphical representations of a variety of data types.
• Perform statistical simulations and appreciate their usefulness and limitations.
• Implement numerical techniques in maximization and integration.
• Demonstrate basic proficiency with R.
• Demonstrate knowledge of basic statistical inference concepts.
• Collaborate effectively with other statistical users and consumers in written reports and discussions.

Software

R.