Unit 1: Blog Post Two

Big Data

When I hear the phrase ” big data,” I automatically imagine a huge, overwhelming spreadsheet full of numbers. The thing is, I’m not exactly wrong. Sure, there’s a lot more to it that numbers, but a vast majority of data collection involves some sort of spreadsheet full of numbers. There’s nothing bad about that, but the mental image could be rather off-putting. However, it’s important to remember that data does not have to be all numbers, and it can be displayed in various ways to make it more mangeable and, frankly, less scary. Bar graphs, line graphs, scatterplots, etc. are ways in which we can take that “big data” and condense it into understandable forms.

I’m a French major with a minor in Elementary Education, and those fields are swimming with “big data.” For French, there are a number of projects specifically that look at text analysis with newspapers, journals, or other forms of print media. There are also linguistic studies, cultural statistics, and online search engines that are full of numerical data. The French langugae encompasses a vast amount of academic study; from linguistics to culture to immigration, it uses data in almost every subject.

For Elementary Education, there seems to be nothing but data. Test scores are organized by subject, grade level, and state, and that data is used to determine which schools are doing well and which schools are falling into the danger zone. Statistics are also kept on proper pedagogical technique, focusing on what works and what doesn’t based on test scores and experiment results. Finally, record-keeping within a school or a school system is what I would consider “big data,” because, over the years, schools develop in-depth corpuses of information that can be used to gather a better understanding of the schools throughout time.

In the end, data is unavoidable, regardless of career or interest area.

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