Data Science Capstone by Johns Hopkins University About this Course The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners.

Capstone for the Data Science Specialization from Johns Hopkins University

Large databases comprising of text in a target language are commonly used when generating language models for various purposes. In this exercise, you will use the English database but may consider three other databases in German, Russian and Finnish. The goal of this task is to get familiar with the databases and do the necessary cleaning. After this exercise, you should understand what real data looks like and how much effort you need to put into cleaning the data. When you commence on developing a new language, the first thing is to understand the language and its peculiarities with respect to your target. You can learn to read, speak and write the language. Alternatively, you can study data and learn from existing information about the language through literature and the internet. At the very least, you need to understand how the language is written: writing script, existing input methods, some phonetic knowledge, etc. Note that the data contain words of offensive and profane meaning. They are left there intentionally to highlight the fact that the developer has to work on them. Tasks to accomplish Tokenization - identifying appropriate tokens such as words, punctuation, and numbers. Writing a function that takes a file as input and returns a tokenized version of it. Profanity filtering - removing profanity and other words you do not want to predict. Tips, tricks, and hints Loading the data in. This dataset is fairly large. We emphasize that you don’t necessarily need to load the entire dataset in to build your algorithms (see point 2 below). At least initially, you might want to use a smaller subset of the data. Reading in chunks or lines using R’s readLines or scan functions can be useful. You can also loop over each line of text by embedding readLines within a for/while loop, but this may be slower than reading in large chunks at a time. Reading pieces of the file at a time will require the use of a file connection in R. For example, the following code could be used to read the first few lines of the English Twitter dataset:

This peer assessed assignment has two parts. First, you will create a Shiny application and deploy it on Rstudio's servers. Second, you will use Slidify or Rstudio Presenter to prepare a reproducible pitch presentation about your application.

Create a web page presentation using R Markdown that features a plot created with Plotly. Host your webpage on either GitHub Pages, RPubs, or NeoCities. Your webpage must contain the date that you created the document, and it must contain a plot created with Plotly. We would love to see you show off your creativity!

Create a web page using R Markdown that features a map created with Leaflet. Host your webpage on either GitHub Pages, RPubs, or NeoCities.

The goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. Specifically, we are going to try to answer two questions: - Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? - Across the United States, which types of events have the greatest economic consequences?