The following is a brief, five-slide pitch that quickly summarizes the text prediction app I built as part of the final capstone project under John Hopkins University's Data Science Specialization.

The following is a milestone report summarizing the exploratory analysis and steps taken thus far to prepare for building an NLP text prediction model/app. As part of the capstone project for the Data Science Specialization offered by John Hopkins University, students are instructed to use web-scraped data provided by SwiftKey in order to build a Shiny app which predicts the next word of a sentence given a few words of input (similar to modern day predictive keyboard features found on iPhones, for example).

My submission for the Developing Data Products course project under John Hopkins University's Data Science Specialization. This is a simple (5 slide limited) pitch for the Shiny app I created which displays and filters average price and total volume data for avocados sold in California over the span of three years.

A very simple demonstration on using the Plotly package in R in order to create interactive plots.

A very simple demonstration on using the Leaflet package in R in order to create custom, interactive maps.

The following is my submission for the Practical Machine Learning course project under John Hopkins University’s Data Science Specialization. In this project, I use accelerometer and gyroscope data from 6 subjects performing barbell lifts correctly and incorrectly in 5 different ways. The goal is to create a model that can successfully and accurately predict the type of movements for the 20 observations in the test set.

The following is my submission for the Regression Models course project under the John Hopkins University’s Data Science Specialization. In this report, we find that there is some evidence to suggest that manual transmissions get better mileage than automatics within our limited dataset. Additionally, we found that a car having a manual transmission results in an increase of ~1.48 miles per gallon (all else equal) in our linear model. With that being said, a much more thorough analysis (with larger sample sizes) is necessary to truly conclude such results with confidence.

This analysis is part 2 of 2 of the Statistical Inference course project of John Hopkins University’s Data Science Specialization course. This second section of the project investigates the ToothGrowth dataset and provides a general exploratory analysis before performing hypothesis tests to determine if there are statistically significant differences in tooth growth by vitamin C dosage and delivery method.

This analysis is part 1 of 2 of the Statistical Inference course project of John Hopkins University’s Data Science Specialization course. This first section of the project will be investigating the properties of the Central Limit theorem as it relates to the distribution of means of 1000 simulated averages for 40 different exponentially distributed values.

In this analysis, I analyzed data from the U.S National Oceanic and Atmospheric Administration’s storm database spanning from the years 1950 to 2011 in order to answer two main questions: 1) Which events are most harmful to population health? 2) Which types of events have the greatest economic consequences?