So you want to get a BS in Economics
It's one of those degrees that sounds impressive on a resume but means very different things depending on which university you attend. At some schools it's basically applied econometrics with heavy calculus. At others it's policy analysis with a statistics minor. I found this out the hard way when I went to interview for a data analyst role and my degree transcript didn't match what the hiring manager expected from a BSc in Economics. The "science" designation means your program will emphasize quantitative methods more than a BA track typically would. Expect microeconomic theory, macroeconomic theory, intermediate and advanced econometrics, linear algebra, and probably a sequences of calculus. Some programs throw in computer science or statistics courses as requirements. The programs I've seen vary wildly between schools. My former program required real analysis as a prerequisite for upper-level econ courses. Another colleague from a different university had a lighter math requirement and more electives in behavioral economics. Neither was wrong, but employers sometimes treat them differently because they honestly aren't comparable.
Here's the thing most people don't tell you before enrolling: the degree itself won't make you employable in analytical roles. What matters is what you can do with the tools the program teaches. I watched classmates graduate with identical degrees and end up in completely different positions because one of them had built a portfolio of projects while the other had only completed assigned coursework. I encountered a specific problem during my junior year when I tried to run a panel data regression for my thesis. The dataset had missing values that weren't random - they were systematically missing for certain periods. Standard listwise deletion would have introduced bias, but the econometrics course we took barely covered the available remedies. I ended up spending three weeks reading papers on multiple imputation techniques before I found that Stata's -mi estimate- command could handle it. The workaround was combining inverse probability weighting with chained equations, which the professor in my advanced econometrics class barely mentioned. Most students in that course would have just dropped the missing observations and moved on, which is technically incorrect but widely done. Another counter-intuitive point that nobody stresses enough: theoretical knowledge and practical application diverge sharply around the time you hit graduate-level coursework. You'll learn models that assume rational agents and complete information, then immediately apply them to datasets where neither assumption remotely holds. The disconnect is intentional in the curriculum design, but it leaves students unprepared for how messy actual research looks.
The bottleneck I see repeatedly is that students treat the econometrics sequence as a checklist rather than a skill-building process. Taking the courses in order without building computational fluency simultaneously creates a compounding deficit. By the time you reach the advanced stats courses, you're struggling with both the theory and the implementation, and the material moves fast enough that falling behind is easy. If you're deciding whether to pursue this, the honest answer depends entirely on what you want to do after. For consulting, finance, or policy analysis, the degree provides a solid foundation. For pure data science work, you'd likely need to supplement it significantly with programming experience. There are online courses and bootcamps that fill the gap, but the math background from the BSc helps where many coding bootcamps don't bother. The downside is that the quantitative rigor comes at the cost of breadth. You'll spend considerable time on proofs and derivations that you may never use in an applied setting. Some programs require economic history or philosophy courses that feel disconnected from the core skill set. A BA in the same field might offer more flexibility for students who already know what direction they want to go.
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