Data Science Is Not Just About Computers: It Is About Decisions

Every year, I ask my Data Science students what has been the hardest part of the course. Invariably, they name coding. (Incidentally, they also name coding the most rewarding aspect). For many students, anxiety about the technical computer science skill sometimes even makes them hesitant to enroll. As I explain to them early on each year, every single student who has ever taken the class completed their project and reached their coding goals. The success rate is literally 100%. 

But I also explain that Data Science — as a discipline — is about so much more than coding and computers. It is also about questioning sources, interpreting data visualizations, understanding uncertainty, communicating conclusions cogently, and learning to sit with the discomfort of not immediately knowing the solution.

The skills students acquire in Data Science will help them make better decisions across nearly all areas of life: evaluating the veracity of a news item, making medical choices, and managing financial decisions. 

One of the most powerful aspects of Data Science is that instead of telling students what to believe, it teaches them to decide how to test a claim. Some of the most nefarious misinformation in the news today is misleading use of true data. A data-literate student who sees sensational headlines knows to ask the right questions: What is being measured? Over what time period? Does the study design justify the conclusions in the headline? Students learn to interrogate claims that conflate correlation with causation, consider potential confounding variables, and to ask whether other factors may explain the observed relationship. They also learn that often what is not shown can be just as important as what is: selective comparisons, cherry-picked time periods, subgroup omissions, and missing contradictory data are all red flags. In short, Data Science equips students with the ability to decide whether a source is trustworthy: a skill that goes far beyond STEM or coding. 

Data Science also prepares students to make more informed medical decisions — a skill that is increasingly essential. Modern health decisions are no longer only made in doctors’ offices. They happen in Google searches, wearable fitness devices, patient portals, and news headlines. Today’s students grow up with smart watches, sleep trackers, period tracking apps, and other devices unavailable to previous generations. Data Science teaches them to distinguish between variability and trends, helping them to recognize that a single abnormal reading is usually meaningless, and over-interpretation is unproductive. Wearable devices often produce numbers with great authority (“Your sleep score: 61.”) Many consumers will naturally assume that if a number exists, it must be both accurate and medically meaningful. Students in Data Science learn to question every metric they encounter. What actually goes into the calculation of a sleep score? Is it movement? Heart rate? Skin temperature? And because students both read and generate data, they learn to ask important questions about privacy: Who owns the data I collect? What does anonymizing data mean? Can it be sold or legally subpoenaed? Many are surprised to learn that privacy laws protecting medical records do not necessarily apply to the data they log themselves. In this way, Data Science transforms decision-making based on health information — increasingly more digital and data-driven — from something overwhelming into something students can approach with confidence and agency.

Another crucial life skill students indirectly learn in Data Science is making better financial decisions. Most adult financial mistakes are not due to laziness or a lack of effort: they stem from misinterpreting percentages, trends, risks, and other numbers. Students are introduced to the idea of variability early: a return has volatility, and typically the higher the return, the higher the risk. They learn that short-term trends are noisy, they question cherry-picked return time windows, they understand exponential growth illusions, and learn to spot scale manipulation. Because students learn modeling, they are less likely to interpret a credit score as a moral judgement and more likely to see it as a predictive model built from historical data. Models, by design, simplify reality, and credit scores, in particular, miss large amounts of context: actual wealth, health events that may prevent payments, and new credit histories. When students understand how financial data is constructed and interpret it correctly, they are far better equipped to make choices that protect and empower their lives.

Ultimately, Data Science is as much about cultivating a mindset as it is about teaching technical skills. It trains students to ask thoughtful questions, critically evaluate evidence, and make decisions grounded in evidence, rather than impulse or assumption. These are skills that extend far beyond the classroom: they guide students how to understand the world, interpret information, care for their own health, navigate financial choices, be stewards of their own data, and engage responsibly as citizens. Data Science prepares students not just to code but also to think – and to make better decisions throughout their lives.