Introduction Essay (Brewer)

My name is Timothy Yeh, and I am a current freshman at the college of William and Mary, hoping to major in business analytics or finance and maybe minor in data science. Academically speaking, I like math, as I enjoy the process of analyzing quantitative data and the feeling of accomplishment from getting a correct answer after lots of hard work. Outside of school, I love playing video games and sports (spikeball, running, taekwondo), hanging out with friends, and just chilling in my room watching a Netflix show. One unusual fact about me is that I know how to juggle.

I believe that data science is the process of studying data, analyzing it, and applying that data into the real world to bring about positive change. This can range from analyzing political data to help improve a candidates’ campaign to keeping track of calorie intake in order to be live a healthy lifestyle. l, I believe that data can have a maximal impact on society if it is put in the right hands and seen from the correct perspective. It is important for data to be in the hands of a company or person who is tailored to analyze that data because by doing so, we can ensure that the data will be used effectively and as intended. Take for example, poll data on a recent candidate. We can’t let that data be analyzed by a 5-star chef of a restaurant because his specific skillsets aren’t intended to be used in such a way that he can effectively draw inferences and patterns from this data. Instead, by putting this data in the hands of a political aide or moderator, we can ensure that the data will be effectively analyzed to make the most effectual change. Secondly, it is important for data to be contextualized because apart from doing so with relation to the audience in question, wrong actions will be taken. In his TED talk, Rosling undermines ideas of generalizing data and pre-conceived ideas. He uses GDP data of individual African countries to show the statistical GDP variation among those countries in order to undermine the statement: “All of Africa is poor”. With this in mind, if companies generalize data, they will develop general solutions that might help one part of their audience but not the other. However, as they learn to contextualize the situations of each section of the audience, they will be able to develop viable solutions that are specifically catered to the needs of the audience and thus have maximal impact.