DATA150

10/28 Essay: To give you a better idea of what data science is and how it compares to other areas in the world of work read this 10-15 min article. Taxonomically, in academia, data science is a subfield of computer science. Personally, I see data science as a tool or a set of tools and methods that a researcher or practitioner of data science applies to data in a particular domain. This combination is their “craft”, in essence. I, for example, apply data science to geography in the context of international development. This means I use tools such as machine learning on data such as satellite imagery to help solve problems in the international development community. Data science is a domain that can be surprisingly hard to define given its broad applications and newness. I don’t necessarily think there’s one correct definition and, whatever it is, the field will continue to evolve with new technologies, new industries, and/or new issues. There’s also a question of skill ranges. Should the title of “data scientist” be reserved for those with doctoral degrees in the same way it is in physics, for example, and anyone below that is basically something else (like an analyst)?

Think about an area of study or field you care about (outside of your formal assignment topic), maybe the major you intend to choose. Based on what you read and know about data science and its related areas (data analysis, stats, and ML), briefly (1-2 paragraphs) write about how you think these disciplines could possibly be used in your field (it’s okay to think ambitiously). If methods are already being used, what are they and to what extent (if you know)? If you are a prospective data science major, what ideas do you have for how data science could be used in ways you believe they are currently not? You have until 10:10.

My intended major is computer science. In my opinion, computer science is a broad field. Some may say that it’s the study of computers and computing. However, I believe computer science is using computers to solve problems. It combines problem solving with mathematics, engineering, and logic. Much like data science, it is constantly evolving as technology advances with those in the field continuously learning. The article, “What is Data Science?,” states that data science is made up of three goals—to provide insight, establish causality, and make predictions. Each goal is associated with the domains of data analysis, statistics, and machine learning. I believe these disciplines are currently used in computer science as well. Computer science uses programming languages to provide insight to create new models of computation and solve complex computing problems. Computer science uses the data we collect to establish causality by applying mathematical formulas and allow us to solve problems. Lastly, computer science makes predictions by creating models of our data. Data science and computer science often go hand in hand. However, data science is a combination of statistics and computer science and requires an understanding of coding, math, and critical thinking.