Chapter 10 of “The Internet” was very oriented toward data and analytics; and while some may find these topics tedious, I rather enjoy looking for patterns and information from a set of data. The concept of metadata, or meaningful information gathered from such analysis, I believe is what makes this science practical. Unstructured data is just chaotic and confusing (like trying to find something in my dresser drawers), and while structured data is more efficient and orderly, a big spreadsheet of numbers or statistics still doesn’t mean much to the average person. Being able to process this data manually, or understanding the ways that a computer does so, gives new relevance to the data values. Instead of a list or compilation, someone can now look at new visual representations, and can understand by average how the data may directly relate to them. As I mentioned in my hobby paragraph, I a big fan of ‘Jeopardy’ and have kept extensive records of winning scores, players, etc. This list covers 7 years and over 500 champions, and is overwhelming at first glance. However, breaking it down into individual seasons, average score by length of winning steak, or average champion age gives more practical information that portrays how the game is played and how it has changed over time.

This chapter also revisits the danger of people twisting data and information to mislead the people that view it. The data may have been poorly gathered and is inaccurate by mistake; or possibly the original intent was to gather data that would slant toward a desired result. Either way, the responsible act of the viewer is not simply to take a statement at face value, but to consider other factors such as the size of the sample, where and from whom the data was collected, and why the analysis was done in the first place. Someone who understands a data model’s method of interpretation can purposefully collect data that will support their side of an argument. Whether it comes to politics, law, finance, or silly online trends, the knowledge from this chapter has better equipped me to deal with the processes and potential hazards of data science.

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