- This presentation shows some features of an application developed during the **Data Science Capstone** course, part of [**the Johns Hopkins Data Science Specialization** on **Coursera**] (http://www.coursera.org/specialization/jhudatascience/1). - It is a Shiny application that takes as input a phrase, and outputs a prediction of the next word. - The user can try it on [https://dsasas.shinyapps.io/Shinyapp](https://dsasas.shinyapps.io/Shinyapp)

This is a milestone report for the last part the Coursera Data Science Specialization: Capstone Project. The goal of this project is to build predictive text models like those used by SwiftKey mobile application.

The app has the purpose to provide an easy tool to that data on deaths and their causes in the world could be analyzed and compared across countries.

This report presents the results of the course project for the Practical Machine Learning course, part of the Johns Hopkins Data Science Specialization on Coursera. The devices Jawbone Up, Nike FuelBand, and Fitbit can collect easily a large amount of personal activity data. People can use these devices to quantify how much physical activities they do, but almost never quantify how well they do it. In the context of [1], six persons were asked to perform barbell lifts correctly and incorrectly in 5 different ways.This report shows how data were used to predict the manner in which they did the exercise (classe variable in the training set).

This report presents the results of the course project for the **Regression Models** course, part of **the Johns Hopkins Data Science Specialization** on **Coursera**. It analyzes the **Mtcars** data in the R datasets package. The data is from the 1974 Motor Trend US magazine and comprises fuel consumption and ten characteristics of automobile design and performance for 32 cars. The goals of this analysis are to: - Check if automatic or manual transmission is better for miles per gallon (*mpg*). - Quantify the MPG difference between automatic and manual transmissions. This analysis was made using regression models and exploratory data analyses, and the findings are as follows: - Manual transmission is better than the automatic. - Cars analyzed with manual transmission can travel 7.24 more miles per gallon on average than the cars with automatic transmission. - Using Multivariable regression analysis, the results reveal that manual transmission cars get 1.4109 miles per gallon more than automatic transmission cars for the same weigt (*wt*) and quarter mile time (*qsec*). - The analysis showed that the variables type of transmission, weigt and quarter mile time influence significantly more variable miles per gallon.

This report is part of the project for the Statistical Inference class in the Johns Hopkins Data Science Specialization by Coursera. This report analyzes the ToothGrowth data in the R datasets package. The goals of this analysis are: Perform some basic exploratory data analyses and provide a basic summary of the data. Compare tooth growth by supp and dose Using confidence intervals and/or hypothesis tests. State conclusions about the data and the assumptions needed for it.

This is part of the project for the Statistical Inference class in the Johns Hopkins Data Science Specialization by Coursera. The exponential distribution can be simulated in R with rexp(n, lambda) where lambda is the rate parameter and n is the number of observations. The mean of exponential distribution is 1/lambda and the standard deviation is also also 1/lambda. It sets lambda = 0.2 for all of the simulations. In this simulation, it investigates the distribution of averages of 40 exponential(0.2)s. This report Illustrates via simulation the properties of the distribution of the mean of 40 exponential(0.2)s. It shows: - Where the distribution is centered at and the comparison it to the theoretical center of the distribution. - How variable it is and the comparison it to the theoretical variance of the distribution. - That the distribution is approximately normal.

The goal of this analysis is to explore the NOAA Storm Database and answer the following questions about severe weather events: Across the United States, which types of events are most harmful with respect to population health? Across the United States, which types of events have the greatest economic consequences?

The goal of this analysis is to explore the NOAA Storm Database and answer the following questions about severe weather events: Across the United States, which types of events are most harmful with respect to population health? Across the United States, which types of events have the greatest economic consequences?