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I received my M.S. in Statistics from CSU East Bay in May 2023, and I currently work as a Data Scientist at Intel in the Foundry Technology Development Analytics and Technology Automation business unit. I'm passionate about bringing people together around common goals of shared learning in diverse and inclusive communities. When it comes to coding, my personal interests include exploring the intricacies of data visualization, mainly using the R programming language.

You can find me elsewhere at:

• 🔗 My blog: Once Upon a Time Series
• 🐘 Mastodon
• 💼 LinkedIn
• 📚 Goodreads

How the R4DS Online Learning Community Made Me a Better Student

Recording | Slide Presentation | GitHub Repo

• Posit Conference, September 20, 2023. Talk Track: Developing your skillset; building your career. Session Code: TALK-1110
• Through my participation in R4DS Online Learning Community, I have advanced my R and data science skills, making me a better student than I otherwise would have been through just my studies. As a non-traditional MS Statistics student with an undergraduate background in economics, I had absolutely no experience with the R programming language prior to pursuing my Master's degree. In July 2021, with hopes of getting a headstart on learning R before beginning my degree program, I joined the R4DS Slack Workspace. Along with helping to improve my programming skills, R4DS has connected me with scholarships, mentorship, and other opportunities, and I think that it would be beneficial for other students to know about this great resource.

Practicality of Using Transformations in Multiple Linear Regression

GitHub Repo | Slide Presentation | Video Presentation

• Final Project for STAT 694 Applied Research in Statistics & Biostatistics, California State University East Bay, Fall 2022
• Compared the multiple linear regression (MLR) model with the inverse transformation dependent response variable to see how much prediction power is lost by not using a transformed response variable to fit a MLR model, and whether it is worth the inability to easily explain your model when using a transformed response variable.

Gender Wage Inequality in STEM

GitHub Repo | Slide Presentation | Video Presentation

• Co-authors: Sara Hatter and Ken Dinh Vu
• Final Project for STAT 632 Linear and Logistic Regression, California State University East Bay, Spring 2022
• Explored the data for STEM college majors to find associations that influence median wages and create a predictive model for median wages.

Data Visualization

GitHub Repo | Tableau | R Pub Notebooks

• STAT 651 Data Visualization, California State University East Bay, Fall 2022