To give you a better idea of what data science is and how is 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?
I’m planning on studying computer science which shares a very similar scope to the field of data science. What is interesting about the two though is that many of the CS applications in the world and in business are evolving or already have evolved to include a great deal of data science. We have analytical and predictive modeling all around us whether it be the Netflix recommendation system using the data from millions of users to suggest shows tailored to you or being able to classify a tumor as malignant or benign by comparing a patient’s data to others. It is hard not to see the expansion of Data science techniques and resources across every single field in one way or another.
With the example of biology and viruses, you might be able to study the statistics of infection with data science and determine potential causality that could help for the future. In the field of government and politics, data science could be used to create a machine-learning algorithm to better predict elections and properly weigh a variety of variables. Data analysis could be used to review a companies advertisements and distinguish what worked and attracted customers and what did not.
One interesting application I could think of for data science could be for high school students seeking higher education. A questionnaire could be filled out by students of their personalities and interests and college preferences and then they could answer another survey once they have decided on a school to go to and settled in. Then using data science and likely neural networks, these results could be used to assign relative values to questions and corresponding schools they might recommend, so future students who take the questionnaire could get the best college recommended to them.
This was just one example that came to mind, but the point is is that the possibilities for data science appear limitless. Whether or not this will become the case or if we will even reach a point with too much data analysis should be interesting to find out. I could see a world where the data and likelihood of any given person doing something is mapped with data science and used in appliances, marketing, quality of life changes, and more. Maybe stoplights could have image classification and count the number of cars traveling in a direction at any time of day and adjust the length of their greenlights to better suit efficiency and travel.