Materials for class on topics in deep learning (STAT 991, UPenn/Wharton)

Github Action wrapper for the Jake Wharton's dependency-tree-diff tool

Shiny mobile app that has users select whether headlines are real or fake (from Buzzfeed survey); connects to mysql DB on EC2

Regularization using LASSO and ridge regression; cross-validation for parameter selection

A lecture on the basics of exploratory data analysis using tidyverse as a TA for Wharton's Statistics Department's STAT701 - Modern Data Mining.

Classification using logistic regression and model selection criteria

Latent-class count models using the NBD and Poisson

Modeling count data using the negative binomial distribution

Diversification, efficient portfolios, capital market line, and CAPM

Applying NBD count models to examine the behavior of Wharton MBA students on the messaging platform GroupMe

Linear regression using different variable selection techniques

NPV, IRR, and capital budgeting

Final project using a survey commissioned by Buzzfeed to identify the factors that best predict whether someone can correctly distinguish between accurate and inaccurate news headlines

Modeling choice data with the beta-binomial and Empirical Bayes

A homework assignment from Seth Stephens-Davidowitz's class called Understanding Behavior with Big Data

Builds Google Sheet (600+ visits) with course and instructor evaluations and clearing prices distributed from the Wharton Analytics Club; visualization to help select courses

Assisted Wharton professors with data analysis for a paper on the changing of brand value over time

Timing models such as the exponential-gamma to measure time to purchase

Building a model to predict whether or not a patient would be readmitted within 30 days after diabetic hospitalization

Text classification using logistic regression, SVM, and random forest; PCA