Bringing Data Down to Size

As discussed in class, data can be noisy, messy and at times difficult to understand. However, using data and data analysis tools we can transform data into information. The key distinction lies in usability. Information contains relevant and easily digested facts and figures. Part of the process of converting data to information involves reducing the volume being presented. In this post, I’ll explore ways to decrease the size and scope of my data-set to make my analysis more manageable.

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Because my data-set centers on U.S. higher education, five key categories stand out as relevant for narrowing down the data: State, Private vs Public, Size, Institution Type, and Financial Aid. For example, I might want to focus on private universities in Illinois with 5,000 to 15,000 students. To further narrow down my analysis, I would focus on pulling the key figures relevant to my analysis, such as cost, employment rate, and debt and earnings levels after graduation. Not only will this narrow down the scope of my data-set, it will allow for more relevant comparisons between similar institutions.

In my next post, I will investigate data analysis tools I can use to visualize and present the information extracted from the data.

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Author: sps_student

Web Development student at Northwestern's School of Professional Studies.

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