Python is a very powerful tool when used properly. The different capabilities it has allows the user to tailor the outcome to any level of expertise needed. This flexibility makes it very useful when used correctly. First, the simplicity of the program allows the user to write their own code from the beginning. This makes it less time consuming to begin learning and using. From there, there are many libraries that one can add for data analysis. These are things like pandas, numpy and scikit learning. These libraries will allow the user to analysis the data using machine learning and compute any number of variables they desire. This plays into the benefits of any nontechnical audiences that the data is meant for. It takes a wide range of complex data, analyzes it, the has the ability to give results that are meaningful. The ability to add visual references of the data represents a quick opportunity for the audience to see the actual data the project represents..