Coursera Capstone Project: Presentation of Shiny App Predictor! as a solution for Data Science Switfkey Capstone Project

Coursera Capstone Project: This report show the step by step output of the execution to finaly create the data tables with the Kneser-ney probability calculations to be used in the Shiny App "Predictor!"

Coursera Capstone Project: Final Report of Capstone Project for Coursera Switkey Shiny Predictor! App

Coursera Capstone Project: this report provide the results of a initial exploratory data analysis of the dataset including ngrams plots.

A Data Explore Shiny App using ITU-T Data and GoogleVis Package.

A Presentation made with Rstudio Presenter for Coursera Data Science Specialization

Coursera Practical Machine Learning: In this report we use the data about personal movement using activity monitoring devices (*Fitbit, Nike Fuelband, or Jawbone Up*) in order to predict the *classe* variable (the manner in which they did the exercise) in the training set.

Coursera Regression Models: In this report we use a data set of a collection of cars in order to try to answer the following two questions: * Is an automatic or manual transmission better for MPG? * Quantify the MPG difference between automatic and manual transmissions?

Coursera Statistical Inference Report 2: Use ToothGrowth data in the R datasets package to answer the following four assignments: * Perform some basic exploratory data analyses * Provide a basic summary of the data. * Use confidence intervals and/or hypothesis tests to compare tooth growth by supp and dose. * State your conclusions and the assumptions needed for your conclusions.

Coursera Statistical Inference Report 1: This project report include statistics simulations to compare the Exponential Distribution in R and the Central Limit Theorem. We will use R in order to make some statistics simulations and compare it with the theoretical values for some special values like mean, variance and distribution.

Coursera Reproducible Research PeerAssessment2: this is a report that use the data of U.S. National Oceanic and Atmospheric Administration's (NOAA) to evaluate the Economic and Population Impact (Fatalities and Injuries) of all the weather events in the U.S. from 1950 and end in November 2011.