If you are coming to the Institute for Advanced Analytics (IAA) from a humanities or social science background, rest assured that you possess a distinct advantage in data science.
By combining technical expertise with your understanding of diverse viewpoints and the ability to contextualize information, you will grow to be a well-rounded data scientist—one who not only understands algorithms but also the human stories behind the data.
Arriving at the IAA with a background in history, I grappled with impostor syndrome. In classes where my peers quickly finished statistics and coding labs, I often found myself working through these assignments much more slowly. I also felt worried about not being able to add value to team discussions because I was not the best coder or the fastest at grasping complex statistical concepts.
But as the weeks went on, I learned that my critical thinking and communication skills are highly valuable in data analytics. I got positive feedback from my teammates about my ability to synthesize different parts of a problem and generate frameworks that helped the team think through complex analytics problems. Using my communication skills, I helped my teammates craft clear takeaway messages using findings from our analyses.
Most importantly, I learned that being able to empathize with your audience and ask what they are most in need of solving is a crucial skill in data science. This skill must be developed in tandem with one’s statistical and coding skills.
People who study humanities and social sciences are trained to empathize with their subject of study. Analyzing why certain historical events happened, interpreting a piece of literature within a time period, or studying the economic practices of a society helps develop skills to examine issues from multiple perspectives, contextualize events, and ask what perspectives might have been left out to make space for another. These are essential skills in data science.
We need to empathize with the end users of our products and consider multiple perspectives and experiences so we can build ethical and inclusive products. This is why, if you are coming to the IAA from a non-STEM field, you have a lot of value to add to the program.
IAA professors often emphasize that data science is as much an art as it is a science. Consider a dataset with missing values; deciding how to handle these missing values requires considering various perspectives and potential impacts. Similarly, let’s say you need to determine the significance level or a cutoff point in a statistical test. You need both technical skills and an understanding of your client’s business context to suggest context-specific cutoffs. In these situations, you need to lean on your empathy, critical thinking, and effective communication skills to make the best ethical, inclusive, and context-specific decisions in your data analysis.
As data scientists, our responsibility extends beyond algorithms and statistical models—it includes the lives impacted by the data that we analyze. Using our empathy, understanding of diverse viewpoints, and the ability to contextualize information, we can bridge the gap between numbers and narratives and craft data-driven decisions that are not just accurate but also ethical and inclusive. So, if you’re entering the IAA from a non-STEM background, your unique perspective is not just valuable—it’s indispensable in shaping the future of data science.
Columnist: Khine Su