Typing Assistant was developed as part of the Capstone project for the Johns Hopkins Data Science Coursera course.

This paper describes a preliminary approach to the Johns Hopkins University Data Science Capstone project. The goal of the Capstone project is to generate an interactive product that takes an ordered series of words as input and predicts and outputs the next word in the series. Given an initial corpus of text data, taken from a set of blogs, news articles, and twitter data feeds, the instructions for the project are to use principles and techniques from the fields of text mining, natural language processing, statistics, and data science to build a model to predict a next word in an ordered series of words, and to build a product in a Shiny app that incorporates the predictive model. Included in this paper are the approaches to basic data cleaning, exploratory data analysis, and initial prediction model ideas and considerations.

This is a pitch for the Shiny app called 2009 Residential Heat Cost Predictor implemented for the Coursera Data Science class called Data Products. The pitch advertises a tool for predicting heating costs in 2009 US dollars based on data from a 2009 residential energy survey administered by the US Department of Energy.

This is a pitch for the shiny app called 2009 Residential Heat Cost Predictor.

Peer Assessment 2 for Reproducible Research