Performs cleaning and statistical analysis of several large unstructured data files, then builds a predictive text model, allowing users to type a sentence and get suggestions on the words they are most likely to use next.

This project serves to analyze a large corpus of text documents to discover the structure in the data and how words are put together. It will perform cleaning and exploratory analysis of the data, then building and sampling from a predictive text model. The final outcome is an application that can take an input text and predict the next word.

An application that demonstrates how common mistakes in data analysis can lead to spurious conclusions.

Johns Hopkins Data Science - Developing Data Products Week 2

This analysis aims to provide answers to questions that help to prepare for severe weather events and prioritization of resources for different types of weather events. In both cases, it shows that according to data from the the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database, tornados are the most harmful weather events.