Spring 2019 - Harvard University
“Sequel to Statistics 139, emphasizing common methods for analyzing continuous non-normal and categorical data. Topics include logistic regression, log-linear models, multinomial logit models, proportional odds models for ordinal data, Gamma and inverse-Gaussian models, over-dispersion, analysis of deviance, model selection and criticism, model diagnostics, and an introduction to non-parametric regression methods.”
STAT 149: Generalized Linear Models
January Term 2019 - Harvard Business School
“This course is an introduction to using statistical approaches to solve business problems. It introduces statistical concepts via a management perspective and places special emphasis on developing the skills and instincts needed to make sound decisions and become an effective manager. The main components of the course include methods for describing and summarizing data, the fundamentals of probability, the basics of study design and data collection, and statistical inference. Data analyses, simulation, and design issues are implemented in the statistical computing package R run within the RStudio interface.”
HBAP: Foundations of Quantitative Analysis
STAT 121b/ CS 109b: Data Science
Spring 2017 & 2018 - Harvard University
Teaching Fellow, Lab Leader (2018)
"Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning."
STAT 139: Statistical Sleuthing Through Linear Models
Fall 2016 & 2018 - Harvard University
"A serious introduction to statistical inference with linear models and related methods. Topics include t-tools and nonparametric alternatives (including bootstrapping and permutation-based methods), multiple-group comparisons, analysis of variance, linear regression, model checking and refinement, and causation versus correlation. Emphasis on thinking statistically, evaluating assumptions, and developing tools for real-life applications."
Academic Development Peer Tutor
Spring 2012 : Spring 2015 - Carnegie Mellon University
Advanced Data Analysis; Principles of Computing; Intro. to Statistics; Intro. Physics 1 & 2