I love teaching statistics and R. If you need to ramp up quickly, review some basic concepts, or get help with a few assignments, reach out!

My expertise includes data analysis, hypothesis testing, statistical significance, regression (simple and multivariate, linear, exponential, logarithmic, and logistic models), ANOVA, t-tests, Z-tests, chi squared, machine learning models, time series, difference-in-difference, econometrics, probability (binomial experiments, normal distribution, geometric distribution, Poisson processes, etc), and more. I can also assist with building, modifying, and improving Shiny apps, especially dashboards for data visualization and analysis. I offer lessons in SAS in addition to R.

I have over 10 years' experience as a researcher and statistician. I have a background in psychology and math, started my career as a researcher at MIT, and worked as an evaluation expert for non-profits before starting a job in the U.S. government as a statistician.

I've been teaching and training others for my entire career. As a graduate TA at UC-Berkeley, I was selected by students as my department's instructor of the year in 2017. In total, I taught three courses at the university level, including an introductory statistics course (with R programming lab) at UC-Berkeley. This course covered topics such as significance testing (Z-test, t-test, chi-squared test), ANOVA, and multivariate regression.

In 2010, I earned a bachelor's degree in math, and in 2017 I earned a master's degree in a quantitative public policy program (e.g. econometrics, statistics for program evaluation, risk and optimization models).

Math and statistics are challenging subjects but can be made a lot easier with good preparation and effective strategies. If you're taking an intro stats, probability, or decision analysis course, learning R/Rstudio for the first time, or need an expert consultant on a coding project, please reach out! I'm looking forward to working with you to build your skills and confidence!