Three ways a graduate degree in the humanities/arts prepares you to work in data science

Turn that Arts degree into a tech job

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Ignore the data science job descriptions that state a degree from a STEM field is required; you should still apply. Sure, your technical skills need to be on point, but your education has more than prepared you to excel in this field.

A degree in humanities is excellent preparation for a job in data science. It is easy to master core data science skills with this type of education.

Well, this is the conclusion I came to after working with art students that wanted to learn computer programming. Their ability to rapidly learn complex topics, be creative and view problems from multiple angles, critically approach new problems, as well as communicate their findings effectively will serve them well in a data science career.

“Learning to learn,” computer programming and critical thinking are keystones of data science, and I argue that a humanities degree has either given you these skills or has wired your brain to make it easy for you to obtain them.

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Learning how to learn

The number one skill a graduate student gains is the ability to learn how to learn. This is the process where one can teach themselves complex topics in relatively short periods because they have mastered how to digest large problems into digestible chucks.

You have perfected it as a graduate researcher, and your newly acquired knowledge is not just a surface level of comprehension. Learning how to learn applies to data science because you will often be asked to tackle problems in areas where you lack domain expertise and must respond quickly with breadth and depth of the topic.

Furthermore, data scientist jobs hops every couple of years, so quickly gaining expertise in a new domain is an asset. Survival in this field is directly related to rapid acquisition of knowledge, as technology is constantly evolving.

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Be Creative

How does a programmer express his creativity? I would argue that programming is more than just logic and is, in fact, a creative endeavor that requires coming up with novel and interesting ways to approach problems.

I teach the same python programming curriculum to postgraduates at two schools, an Art College and a major University. At the art school, 98% of the students have a humanities/art degrees while it is a mixture of humanities/art and STEM at the university. When comparing the students, the art and humanities students’ performance in the course at both schools surpasses those of the others. Art students can easily grasp abstract concepts that STEM students struggle with. The art students’ ability to develop different ways of generating imaginative solutions to rather simple problems is astonishing.

The art students’ rapid rate and level of mastery at the end of the curriculum outperforms that of the STEM postgraduates. It is also important to note that all students regardless of degree are high quality students.

I acknowledge that my analysis could be a gross generalization, and there may be other confounding factors that can explain away the students’ differences. However, I have taught this class several times and each time the results have been the same. To overcome these differences and produce the best students possible, a new and innovative initiative at the university was put into place that teach students how to think.

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Think Critically

According to,

“critical thinking is that mode of thinking — about any subject, content, or problem — in which the thinker improves the quality of his or her thinking by skillfully analyzing, assessing, and reconstructing it.”

The various disciplines in the humanities use critical thinking when they show us how to listen, how to analyze, how to argue, and how to navigate our social world. Similarly, in data science, you apply this method to generate testable hypotheses and possible solutions. Your thinking skills are directly transferable to scientific thinking in data science.

Final Thoughts

Data science requires more diverse backgrounds and ways of thinking in order to achieve true innovation. You already have or can build the skills needed to begin your journey as a data practitioner. I have faith in you. Remember, programming is a lot easier than you think.


This article addresses graduate students, but for college students trying to decide what to study, educator Dr. Tonya Howe thinks college students that want to get into tech should major in both in the humanities and sciences.

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