Advice for undergraduate students

There's an old saying: free advice is worth what you paid for it. There's something to that, and in any case you should make your own decisions. But we (meaning myself and several of my faculty colleagues) have put together a FAQ sheet giving you some things to think about as you plan your undergraduate career and beyond. None of this reflects official Stern or department policy, but we nevertheless believe it to be sound advice. Also free.

This is one of a collection of advice pages, all of them with the same theme: get skills, especially quant skills. They include advice for undergrads (this one), advice for MBAs (soon!), thoughts about data science (think "big data"), and advice for students who think they might be interested in applying to graduate programs in economics or finance.

Q1. What courses should I take?

NYU offers what you might call "the curse of choice": there are so many courses to choose from that it's hard to know where to start. We think you should take lots of things while you're here, expose yourself to a broad range of perspectives. While you're doing that, leave some time to take courses that develop skills that you can’t pick up yourself, either on the job or on your own. That’s the beauty of courses, they provide structure to help you learn efficiently. Chris Blattman puts it this way: "Acquire skills that are hard to get outside school."

We think quantitative courses would head the list. They're hard to learn outside of class, and they're useful in almost any line of work you decide on. If you have a particular talent for math, you might want to take lots of them, but we think everyone would benefit from taking a few. You'll find that it's fun to have skills. And to repeat: quant skills prepare you for almost any career you might choose: finance, consulting, marketing, or even working for a nonprofit or professional sports team (yes, we have graduates doing all the above). If that doesn't persuade you, just look at these links. If you're in a hurry, they say that people with quant skills get paid more. We know, money isn't everything, but this is information you might want to know up front.

To be clear: we're not suggesting you become a quant, although that's something to think about. But having even some basic quant skills will give you a new perspective on the world. And it's a useful perspective: quant skills are an identifiable source of differentiation that can get you the job you want, even if you go on to do other things. The first three slides contain a shorter version of the same message.

Q2. What if I plan to go into finance or consulting?

Quantitative and data skills are invaluable in both fields -- and many more besides. It's no accident that banks, hedge funds, and consulting firms hire people with math and science backgrounds as well as business students. A business school education is a wonderful starting point, but business school + quant skills is unbeatable -- "priceless," you might say.

Q3. Quantitative course options

Here's a quick overview, but if you're interested in information about specific courses, ask around. If you come up with good ideas of your own, please pass them on.

Programming and computer science. Everyone uses software in the modern world, but you're kidding yourself if you think that means Excel. You give yourself a huge advanatge if you learn to write programs -- to code, as they say. A Yale student put it this way: "Code is the lingua franca of the 21st century." If you don't believe us, ask Miss Disruption or the MathBabe. Or ask the head of the University of California system, who says: "At Berkeley, about 70 percent of students are taking some computer science. At Stanford I think it's 90 percent, but that's Stanford. We're trying to introduce data science and data analytics into the core arts and sciences curriculum."

A good place to start is Introduction to Computer Programming (CSCI-UA.0101, uses Python). Or you could take our Data Bootcamp course (ECON-UB.0232, Python applied to economic and financial data). Or you could teach yourself Python; see our data science page. We like Python, but the language matters less than learning one. We see C++, Python, Matlab, and R used throughout the business world. See, for example, this speech about data analytics from the SEC.

Mathematics. The Stern School requires one semester of calculus, but we think you'd benefit from doing more than that. We recommend more calculus -- and linear algebra. They’re the foundation of economics, finance, and data analysis of all kinds. Especially linear algebra. Once you’ve done that, you're well positioned to do lots of things. And perhaps even to understand that math is an art. And appreciate xkcd.

Data analytics. The modern world generates enormous amounts of data. Whatever you do in the future, it will be extremely helpful to you to develop the skills to make sense of it: programming, math, probability and statistics, data mining, and so on. Some people call the emerging field "data science," but whatever you call it, it involves marketable skills. See our guide to data science for more.

Q4. Are quantitative courses hard?

Grading in quantitative courses tends to be less forgiving than in less quantitative subjects. That's one of the reasons they're so helpful: you learn to think precisely and get clear feedback on whether you have done that.

Could this lower my GPA? Sure it could, although we find that many of our students do well in these courses. (Remind yourself: you're a good student or you wouldn't be here.) But even if your GPA falls, you'll expand your career options, raise your earning potential, and maybe even have some fun. Let us repeat: this will expand your career options. Businesses hire people who have skills that help them, and a GPA is not a skill.

One last thing: It can be a mistake to take quant courses that are too advanced -- and there are always courses that are too advanced. Take a course that stretches you but doesn't kill you. Once you’ve done that, you can take another one. Talk to your classmates about which courses and teachers they recommend.

Q5. Anything else?

You can learn a lot outside the classroom -- and usually have some fun doing it. NYU is loaded with active student clubs, but we're more familiar with faculty activities. You have access to 200+ faculty members at Stern and many more throughout the University. Many of them are happy to include students in their work. Whether your interests are economics, finance, marketing, social psychology, or something else, you should be able to find a faculty member with similar interests who could use help in research or course development. Some students find that experiences like this are the highlights of their undergraduate careers.

Q6. How can I find research assistant opportunities?

We suggest you do the following:

  • Skim the personal home pages of the faculty in a department that interests you.
  • Note those whose work sounds interesting.
  • Knock on their doors and ask if they could use help in any of their projects.
  • If that doesn’t work, return to the previous step. Continue as needed.
  • Having a skill may help. In economics and finance, programming experience is often useful.
  • If you get discouraged, come see me. Or see Professor Foudy, who's right next door and knows many of you from the EGB course.

Lots of people have found work this way. Often you will have to start by working for free, but once you've established your value you may be able to get paid. But remember: the value here isn't the money, it's the experience.

There are also research opportunities outside NYU. Federal Reserve banks typically have both summer internships and full-time research assistant positions for students after graduation. Fed jobs are an established route to success: students who do this for a couple years often move to good jobs in industry or apply to graduate school. Many universities offer similar opportunities.

Q7. Should I take economics courses?

It's up to you, there are many routes to success. We think economics provides a solid foundation whether you want to work in consulting, financial services, marketing -- or lots of other areas. If data provides information, economics provides a context for interpreting information. NYU is blessed with two unusually strong economics groups -- one at Stern, the other in the College of Arts and Science -- as well as a world-class finance group. It's a resource you might want to take advantage of, whether you choose it as a concentration or just take a few courses.

If you're looking for the quantitative side of economics, we have several courses in what we call the Frontiers of Economics. All of them use quantitative tools -- code, math, or both -- to attack problems in economics and finance. They'll give you a sense of how these tools are used in the business and beyond. Current offerings include:

  • Data bootcamp (ECON-UB 232). Economic and financial data and the tools needed to make sense of it. Includes a practical introduction to Python. No math requirement.
  • Macroeconomic foundations for asset prices (ECON-UB 233). Connections between asset prices (equity, options, bonds) and the economy as a whole. Combines economics, math, and Matlab. Requires Calculus 1 or the equivalent. Usually offered in the fall.
  • Advanced topics in modern macroeconomics (ECON-UB 234). Models of information acquisition and forecasting with applications to economic conditions and asset allocation. Requires Calculus 1 or the equivalent. Usually offered in the spring.
  • Quantitative economics. CAS sometimes offers a course in dynamic macroeconomic theory with Python. It's a great course -- and a demanding one. Requires Calculus 1 or the equivalent.
  • SQL bootcamp. Our former students tell us that familiarity with SQL databases is indispensible in the business world, so we ran a non-credit course in April 2015. You can access all of the material, including videos, at the link. No math requirement. If you'd like just a taste, Kahn Academy has a nice intro.

Q8. How does the economics concentration work?

The place to start is your advisor. If that doesn't work to your satisfaction, speak to Professors Foudy and Wachtel.

If you have other questions, particularly about the quant side of things, stop by and say hello. I'll leave it to you to find the way.

More advice: Data Science | Graduate Programs | MBA Advice

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