I had the privilege to be a Teaching Fellow in the Fall 2023 semester, taking on the role of instructing the inaugural offering of Engineering 080 (ENGR 080). Throughout this experience, I independently undertook the development, delivery, and administration of all course materials.

The primary objective of this course was to provide a practical and hands-on introduction to probability theory and statistics. In ENGR 080, the central focus involved introducing statistical concepts during lectures and subsequently applying them to either example problems or case studies incorporated into Jupyter modules that I had developed.

The following topics were covered in the course

1. Role of Statistics in Engineering
2. Designing Experimental Studies to Collect Data
3. Describing Data (Selection and Application of Data Visualizations)
4. Error Analysis
5. Probability (Foundational Laws, Conditional, and Bayes)
6. Discrete Probability Distributions (Binomial and Poisson)
7. Continuous Probability Distributions (Normal and Exponential)
8. Inferences about Population Central Values (p, t, and z tests)
9. Inferences about Population Variances (ANOVA/F-tests, $\chi^2$ tests)
10. Categorical Data
11. Linear Regression and Correlation
12. Multiple Regression (Includes Logistic Regressions and Linear Transformations)

The course comprised four key components aimed at strengthening our students' foundations in probability theory and statistical modeling:

1. Lecture: I conducted weekly sessions to introduce and elaborate on the content, providing students with opportunities to practice newly introduced concepts during the lecture.
2. Discussion: This segment involved students applying their knowledge from the lecture to solve practice problems in a group-based environment, fostering collaborative skill development.
3. Homework: Students independently tackled problems based on the content learned in the preceding week, reinforcing their understanding and mastery of the subject matter.
4. Jupyter Modules: At the conclusion of each unit, students engaged in reading and analyzing case studies presented in Jupyter notebooks. These modules applied the statistical concepts introduced in the unit, while also acquainting students with the Python data science and visualization stack.

I extend my sincere appreciation to the Bioengineering department at UC Merced for granting me the opportunity to contribute as an instructor. This experience has allowed me to showcase my capabilities in leadership, instruction, and guidance, particularly in imparting knowledge on the application of statistical methods and principles to the academic and professional pursuits of UC Merced undergraduates.