Commit 790e797

Sometimes you just need to see it to understand how data analysis,

visualization and storytelling can intersect in a meaningful way. The

following resources do a great job of telling stories with data. There

are more links to stories listed on the [data analysis](/data-analysis.html)

and [data visualization](/data-visualization.html) pages.

* [Metadata Investigation : Inside Hacking Team](https://labs.rs/en/metadata/)

presents what metadata is and how it can be used to track people even though

it is often thought of as less of a problem than typical stored data.

* [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)

provide a spectacular example of

[data visualization](/data-visualization.html) to explain what a machine

learning model does on a San Francisco and New York housing data set.

* [Earthquake recurrence and survival analysis: How long should we wait for an overdue earthquake?](http://rocksandwater.net/blog/2016/07/wrightwood-recurrence/)

combines earthquake data with questions around earthquake recurrence

probabilities to tell its story.

Looking for freely-available data to use in your projects but aren't

sure where to get it? The following links have large free, open data

sets.

* Check out the

[awesome public datasets](https://github.com/awesomedata/awesome-public-datasets)

project repository for data in many different categories ranging from

finance to museums.

* [Kickstarter datasets](https://webrobots.io/kickstarter-datasets/)

are scraped JSON and CSV structured monthly data from Kickstarter