The goal is to build a machine learning to predict NC election results based on early voting turnout. The premise is that over the past 12 years of available data there should emerge a trend or relationship between early voting turnouts at the precinct/county level and voter turnout on actual election day.

The goal of this report is to predict election day turnout and results in NC using early voting data.

Using a online accessible business dashboard eliminates the need for sending around files and the potential of altering a document in irreperable ways. This app is a proof of concept for a simple scenario testing dashboard for internal company projects. The inputs are divided by the phase of the project as in Startup, Operation, and Shutdown. In each phase different inputs can be adjusted and changed. All output are fully reactive.

This is the course project for the Getting and Cleaning Data Coursera course. The R script, run_analysis.R, does the following: Download the dataset if it does not already exist in the working directory Load the activity and feature info Loads both the training and test datasets, keeping only those columns which reflect a mean or standard deviation Loads the activity and subject data for each dataset, and merges those columns with the dataset Merges the two datasets Converts the activity and subject columns into factors Creates a tidy dataset that consists of the average (mean) value of each variable for each subject and activity pair. The end result is shown in the file tidy.txt.

This an R markdown file generated for completion of the Coursera Reproducible Research Course Project 1. The following computations and visualizations are performed using the ggplot2, dplyr, and scales packages. https://www.coursera.org/learn/reproducible-research/.

Make a plot that answers the question: what is the relationship between mean covered charges (Average.Covered.Charges) and mean total payments (Average.Total.Payments) in New York? Make a plot (possibly multi-panel) that answers the question: how does the relationship between mean covered charges (Average.Covered.Charges) and mean total payments (Average.Total.Payments) vary by medical condition (DRG.Definition) and the state in which care was received (Provider.State)?

Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008. Upload a PNG file containing your plot addressing this question.

Exploratory graphs of global active power from sub metering data on certain days of the year.

R programming project for sorting hospital data.

Plotly Presentation Assignment: Developing Data Products

By accessing the Open Data NYC api we can look at art museum locations in New York City. Points are clickable for more information.

makeCacheMatrix: This function creates a special “matrix” object that can cache its inverse. cacheSolve: This function computes the inverse of the special “matrix” returned by makeCacheMatrix above.

Write a function named 'pollutantmean' that calculates the mean of a pollutant (sulfate or nitrate) across a specified list of monitors. The function 'pollutantmean' takes three arguments: 'directory', 'pollutant', and 'id'. Given a vector monitor ID numbers, 'pollutantmean' reads that monitors' particulate matter data from the directory specified in the 'directory' argument and returns the mean of the pollutant across all of the monitors, ignoring any missing values coded as NA.

This is the optional quiz from week 3 of Coursera’s Regression Models class within the Data Science Specialization.

This is Quiz 4 from the Statistical Inference course in the Data Science Specialization from John's Hopkins.

This is Quiz 3 from the Statistical Inference course in the Data Science Specialization from John's Hopkins.

This is Quiz 2 from the Statistical Inference course in the Data Science Specialization from John’s Hopkins.

This is Quiz 1 from the Statistical Inference course in the Data Science Specialization from John’s Hopkins.

This is Quiz 2 from the Reproducible Research course within Coursera's Data Science Specialization.

This is Quiz 1 from the Reproducible Research course within Coursera’s Data Science Specialization.

This is Quiz 1 from the Exploratory Data Analysis course

This is Quiz 2 from the Exploratory Data Analysis course within the Data Science Specialization on Coursera.

This is Quiz 3 from the Developing Data Products course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is Quiz 2 from the Developing Data Products course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is Quiz 1 from the Developing Data Products course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

With today’s sensor technology phones can teach use to salsa or watches can teach us how to eat salsa effectively. As you might expect, applications are not limited to only salsa-related activities.

This is Quiz 4 from the Getting and Cleaning Data course within the Data Science Specialization on Coursera.

This is Quiz 2 from the Getting and Cleaning Data course within the Data Science Specialization on Coursera. Topics include querying an API and SQL database, html scraping, and fixed width files.

This is Quiz 3 from the Getting and Cleaning Data course within the Data Science Specialization on Coursera. Topics include sorting, matching, and aggregating data.

This is Quiz 1 from the Getting and Cleaning Data course within the Data Science Specialization on Coursera. Topics include reading XML, excel files, and extracting data.

This is Quiz 4 from the R Programming course within the Data Science Specialization.

This is Quiz 3 from the Data Scientist's toolbox course within the Data Science Specialization.

This is Quiz 4 from the Machine Learning course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained. Topics include machine support vectors, forecasting, ensembling, random forests, and boosting.

This is Quiz 2 from Coursera’s R Programming class within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained. Datasets are available in R packages.

This is Quiz 1 from Coursera's R Programming class within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained. Datasets are available in R packages.

This is Quiz 3 from the Machine Learning course within the Data Science Specialization. This is intended as a learning resource. Topics include Random Forests, CART, k-fold cross validation, bias and olive oil.

This is Quiz 2 from the Machine Learning course within the Data Science Specialization. Contents include Principle component analysis, variable transformations, and the caret package. This is intended as a learning resource.

This is Quiz 1 from the Machine Learning course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is Quiz 1 from Coursera's Regression Models class within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained. Datasets are available in R packages.

This is Quiz 2 from Coursera's Regression Models class within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained. Datasets are available in R packages.

Goldilocks! This optimal fit accounts for almost the same amount of variance as the first model, AND has a significant P-value (for our purpose).

This is Quiz 4 from Coursera’s Regression Models class within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained. Datasets are available in R packages

This is Quiz 3 from the Data Scientist's toolbox course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is Quiz 2 from the Data Scientist's toolbox course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is Quiz 1 from the Data Scientist’s toolbox course within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is Quiz 3 from Coursera’s Regression Models class within the Data Science Specialization. This publication is intended as a learning resource, all answers are documented and explained.

This is a brief graphical and numerical exploration of a data set. The goal is to summarize the data and perform hypothesis tests at 95% confidence intervals.

R is loaded with powerful methods for simulating, modeling and analyzing data. In this document I give a basic simulation of exponential data and validate the data against a theoretical model.

This analysis is a brief exploration of the U.S. National Oceanic and Atmospheric Administration's (NOAA) storm database. The primary goal of this analysis is to answer the following two questions: 1. Across the United States, which types of events (as indicated in the