Task 7 - Slide Deck The goal of this exercise is to "pitch" your data product to your boss or an investor. The slide deck is constrained to be 5 slides or less and should: (1) explain how your model works, (2) describe its predictive performance quantitatively and (3) show off the app and how it works.

The goal of this project is just to display that I am proficient in working with text data and that I am on track to create my prediction algorithm. This milestone report explains my exploratory analysis and my goals for the eventual app and algorithm. This document explains only the major features of the data and briefly summarize my plans for creating the prediction algorithm and Shiny app. This report makes use of tables and plots to illustrate important summaries of the data set. The motivation for this project is to: 1. Demonstrate that I’ve downloaded the data and have successfully loaded it in RStudio. 2. Create a basic report of summary statistics about the data sets. 3. Report any interesting findings that I discovered so far. 4. Get feedback on my plans for creating a prediction algorithm and Shiny app.

This project was created to satisfy the requirements of the Coursera Developing Data Products Course, the 9th course in the JHU Data Science Specialization Certification.

A Shiny project to satisfy the requirements of the Coursera Developing Data Products Course, the 9th course in the JHU Data Science Specialization Certification.

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.

This article has two objectives: (1) visualizing spatial and temporal trends of criminal activity based on data, (2) analyzing factors that may affect unlawful behavior based upon the data. Taken together, we are really using R programming to cluster and describe data that the police departments and/or city halls have already collected and turned over to the skillful hands of the analyst.

This paper is published to satisfy a requirement for JHU's Reproducible Research course. It is a study comprised of two analysis questions: 1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? 2. Across the United States, which types of events have the greatest economic consequences? Using NOAA weather data, Tornado events have the greatest impacts on population health, both fatalities and injuries, and exceed other events at least three-fold. For property damage, flood events (widespread) is overwhelmingly the leader, while the major cause of crop damage is drought.