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Fortunately, the issue of data management and analysis is not endemic to astronomy, and the resulting overlap with the needs of the broader scientific community and the industrial community at large provides a large pool of scientific software developers to tackle these common problems.  In recent years, this broader community has settled on python as its programming language of choice due to its efficacy as a `glue' language and the rapid speed of development it allows.   This has led to the development of a robust scientific software ecosystem with packages for numerical data analysis like \code{numpy} \citep{Oliphant2006,VanderWaltColbertVaroquaux2011}, \citep{VanderWaltColbertVaroquaux2011},  \code{scipy} \citep{JonesOliphantPeterson2001}, \code{pandas} \citep{McKinney2010}, and \code{scikit-image}; \code{matplotlib} \citep{Hunter2007}, and \code{seaborn} for plotting; \code{scikit-learn} for machine learning, and statistics and modeling packages like \code{scikits-statsmodels}, \code{pymc}, and \code{emcee} \citep{Foreman-Mackeyetal2013}. Python is quickly becoming the language of choice for astronomers as well, with the Astropy project \citep{Robitailleetal2013} and its affiliated packages providing a coordinated set of tools implementing the core astronomy-specific functionality needed by researchers.   Additionally, the development of flexible python packages like \code{yt} \citep{Turketal2011} and \code{pynbody} \citep{Pontzenetal2013}, capable of analyzing and visualizing astrophysical simulation data from several different simulation codes, have greatly improved the ability of computational researchers to perform useful, insight-generating analysis of their datasets.