This is a presentation for Coursera's Data Science Capstone.

The goal of this project is just to display that you've gotten used to working with the data and that you are on track to create your prediction algorithm. Please submit a report on R Pubs (http://rpubs.com/) that explains your exploratory analysis and your goals for the eventual app and algorithm. This document should be concise and explain only the major features of the data you have identified and briefly summarize your plans for creating the prediction algorithm and Shiny app in a way that would be understandable to a non-data scientist manager. You should make use of tables and plots to illustrate important summaries of the data set. The motivation for this project is to: 1. Demonstrate that you've downloaded the data and have successfully loaded it in.2. Create a basic report of summary statistics about the data sets.3. Report any interesting findings that you amassed so far.4. Get feedback on your plans for creating a prediction algorithm and Shiny app.

This is my reproducible pitch presentation for Coursera's Developing Data Products course project.

A 3D Scatterplot of the mtcars dataset using Plotly. The plot will show the weight of the car on the x-axis, horsepower on the y-axis, and miles per gallon on the z-axis. Each marker will be color coded by transmission.

This is my week 2 assignment submission for the Developing Data Products Coursera course by Johns Hopkins. It contains a map of Washington D.C. that labels each Smithsonian museum on the National Mall and provides a link to its website.

Severe weather storms can cause significant population health and economic damage. We will use data from the NOAA Storm Database and answer two important questions. First, which types of events are most harmful with respect to population health? Second, which types of events have the greatest economic consequences? We answer these questions by finding the top 10 types of events that contribute to these measure using the number of fatalities, the number of injuries, and the property damage. We found that tornadoes were the most harmful to population health, and tornadoes and thunderstorm winds had the greatest economic consequences.