Why a liberal arts degree gives you an edge in data science

There’s a misconception that people aspiring to work in technical fields need to have a technical background. A data scientist should have a degree in analytics, statistics, or data science. An aspiring computer scientist should only focus on learning how to code. 

This couldn’t be further from the truth. I’m here to argue that one of the most critical things you can do to stand out in a technical industry like data science is studying liberal arts.¹ Focusing on a qualitative degree hones your soft skills, particularly your ability to reason, to communicate effectively, and to bridge across disciplines.

Liberal arts hones your reasoning skills

So much of a liberal arts degree is about learning how to think — building an understanding of what you’re reading by reasoning through arguments or ideas. For example, after reading a seminal text in a philosophy class, a classic assignment is breaking down the author’s argument, developing a point-of-view that agrees with or disagrees with the author, and explaining why you believe in this position. 

Data science works in the same way. In order to be an impactful data scientist, you must understand the context behind the data, draw conclusions based on a set of data points, and tell a story using data to convince the business of which direction to go next. Consider, for example, you’re tasked with looking into retention trends on a dating app. A downward trend in retention metrics can indicate a negative sign (higher user churn) or a positive sign (more people finding their forever partner). The story you tell with this data, the conclusions you make, and the suggested next steps could be markedly different. For the dating app, a negative conclusion might mean developing win-back strategies for churned users, while a positive conclusion might suggest developing a quantitative or qualitative survey to understand what is working so well with successful matches to double-down on this approach.

Liberal arts makes you a better communicator, both in written and verbal form

One of the key skills of a successful data scientist is sharing your work in written form. Decks, research papers, notes, and summaries are an important medium used to document and share to business and technical stakeholders. 

When I first wrote a data science note summarizing my research and analysis, I ordered my note in the order I did the work: my hypothesis, methodology, datasets used, analysis, and conclusions. This led to a rather verbose note that was lost in the shuffle of other employees’ write-ups and announcements posted at the same time. Over time, I learned to focus on writing research notes similar to how I approached my undergraduate thesis in Philosophy, Politics, and Economics—start with the problem statement and conclusion and then state how I got to it (with technical details in an associated appendix). As a result, my writing became more cogent and succinct, boosting readership and engagement in the process.

Along with communicating your thoughts via writing, data scientists often present work verbally. In meetings, convincing leadership and cross-functional partners of where the business should go next requires adept skills in respectful debate, acknowledging concerns and providing mitigation ideas, all while managing time constraints and sticking to an agenda. 

I was a part of Claremont McKenna College’s Model UN team, where we discussed and debated international and political issues spanning countries, adapting the persona and perspectives of a particular country. Connecting the dots looking backwards, I strengthened my speaking and presentation skills through Model UN — my ability to listen to and empathize with other perspectives, diplomatically articulate and convince others of my opinion, all while shaping the strategic vision and goals to solve the international issue at hand. This has been an accelerator in my career trajectory in part because these skills are required of senior data scientists — adjusting your communication style to different audiences, presenting to and convincing senior leaders, and shaping strategy and direction.

Liberal arts allows you to bridge across disciplines

The classes you take in liberal arts degrees are of varying disciplines, spanning from history to literature, from philosophy to mixed-media art. So too are the technology products created but also are the users of those products. 

Three poignant examples:

• At Meta, one of my data science colleagues is a skilled ceramist. She fashions mugs, bowls, and other dish-ware with clay just as one fashions a meaningful graph with pixels to illustrate a trend and draw conclusions in analytics. 

• The phrase “history repeats itself” is a refrain that resonates with roadmaps and quarter-planning in tech. Like a swinging pendulum, my work in small business ads has shifted from a focus on all advertisers to a focus on low- or top-spending advertisers and back again. 

• Drawing a parallel with entertainment and cinema, the complex, competing emotions in Pixar’s Inside Out mirrors the complex relationship and emotions people have with social media. While some highlight the negative effect social media has had on mental health, others have used social media to change their lives — to meet their spouse, to connect meaningfully with family and friends far away, or to find community with shared interests.

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Ultimately, college students should study what they are passionate about, not what will be the most employable or the most lucrative opportunity after graduation. But instead of doubting if majoring in liberal arts precludes you from entering a technical field, consider the unique skills liberal arts brings to the industry and share how your unique perspective is a strength to prospective employers. After all, what is science without art.

1. This is not to say that technical skills are not critical in the industry. To stand out, you either have to be exceptional or be different, the latter of which is easier in my opinion. From my experience in the industry, there is a dearth of data scientists from liberal arts backgrounds.