Choosing final class for econometrics minor

[hockeyhead]

I am about to register for my spring courses for my undergrad junior year at UIUC. I need one more course to finish my minor in econometrics and having trouble choosing between Financial Econometrics and Applied Machine Learning in Econ. Alternatively, I can talk to an advisor to take a graduate course in econometrics. What do you think would be more applicable to a career as a quant? I am pursuing a dual degree in physics and math with a minor in statistics. Thank you so much for any help! Also each course description is below.

ECON 472 - Financial Econometrics Examines the econometric modeling applied to empirical and computational finance. Explains the empirical properties of financial data as well as the statistical models behind these stylized facts from the data. Explains the statistics and time series concepts that will be useful to understand financial market dynamics, and investigates some popular econometric models and estimation methods.

or

ECON 490 - Applied Machine Learning: Econ This course gives an overview of different concepts, techniques, and algorithms in machine learning and their applications in economics. We begin with topics such as classification, linear and non-linear regressions and end with more recent topics such as boosting, support vector machines, and Neural networks as time allows. This course will give students the basic knowledge behind these machine learning methods and the ability to utilize them in an economic setting. Students will be led and mentored to develop and solve an economic problem with machine learning methods introduced during the course.

or

ECON 532 - Econometrics Analysis I Theoretical treatment of economic statistics. Covers probability theory, set theory, asymptotic theory, estimation and hypothesis testing.

 

[Daniel Duffy]

What do you program it on? Any maths modules?

 

[hockeyhead]

What do you program it on? Any maths modules?

The financial econometrics is in R but non-coding intensive. The machine learning in econ is all in python while in the graduate course I have no clue about. I do not know what kind of maths they may or may not contain.

 

[noether-skolem]

Between those I'd lean towards the applied ML. Python is more commonly used (and demanded) in the non-academic world, at least going by job descriptions. Having "ML" on your resume is nice too, it's one of the buzzwords recruiters seem to like.

Then again you should pick whichever you're more interested in, you can learn python/ML from online courses, certifications, or on the job.

 

[Onegin]

I'll take the other side of the trade from my respected colleague @noether-skolem.

Key Assumptions: Junior year, so you'll have another year to take some elective courses; Planning to go to work right after school; have access to coursera

I'd vote ECON 472 - Financial Econometrics. You'll gain a solid foundation in a useful subject needed for a lot of fields in finance. The Grad class would be better if you're planning on doing an MFE, but I suspect it's going to be very heavily math based. Check w/ professor / syllabus to see if you can hang.

The ML course is definitely a must, but might be better senior year. If your school has a pure ML / DS sequence, I would opt for that over the "econ + ML" Very often the field specific level courses cover ML in a recipe-book fashion with limited time to really understand important concepts in ML. You can get the same basic exposure over winter break w/ a few coursera courses. The higher level stuff is harder to find.

The only compelling reason to take ML is if you haven't scored an internship. That course will be more of an attention grabber, and likely help w/ recruiting.

 

[hockeyhead]

I'll take the other side of the trade from my respected colleague @noether-skolem.

Key Assumptions: Junior year, so you'll have another year to take some elective courses; Planning to go to work right after school; have access to coursera

I'd vote ECON 472 - Financial Econometrics. You'll gain a solid foundation in a useful subject needed for a lot of fields in finance. The Grad class would be better if you're planning on doing an MFE, but I suspect it's going to be very heavily math based. Check w/ professor / syllabus to see if you can hang.

The ML course is definitely a must, but might be better senior year. If your school has a pure ML / DS sequence, I would opt for that over the "econ + ML" Very often the field specific level courses cover ML in a recipe-book fashion with limited time to really understand important concepts in ML. You can get the same basic exposure over winter break w/ a few coursera courses. The higher level stuff is harder to find.

The only compelling reason to take ML is if you haven't scored an internship. That course will be more of an attention grabber, and likely help w/ recruiting.

I appreciate your thought out response. Because of your guys help and more information on the courses I am planning on ECON 472 financial econometrics because the machine learning course does not have a lot of depth.

I am unsure if I will pursue a MFE but I am hoping to find a job after undergrad. With your advice I am planning my statistics electives to be ML/DS class and a stochastic process class. Do you think this is a good plan?

I do not currently have an internships but I think I will be able to get offers soon. I will definitely study some machine learning over break to add it to my resume. @noether-skolem What would you guys recommendation coursera, certification, or textbook wise to study ML over break?

Again I am super duper appreciative of both of your guys input this helps a lot!

 

[noether-skolem]

I have no recommendations for ML courses (never studied it formally, I'm taking ML course in Spring at Columbia MSFE). If your school has ML courses offered by the CS or math departments those might be your best bet in terms of rigor and depth, but keep in mind several things: you may or may not have the prerequisite math/CS background to tackle those; you may not even need to go so far in rigor/depth for the application you're interested in doing - just like how you don't need to understand the measure theoretic formulation of probability theory to do regressions with real world data.

I think it's a good thing that you plan to find a job after undergrad before jumping into grad school. It's a big commitment in terms of cost and time, and it may not even be necessary if you could go straight into the jobs you want/like. After working for a couple of years you'd have a better understanding of what you want to try, and you can think about grad school afterward (who knows you may not even be interested in the career paths MFE programs open, MBA, MFin, CS, or other degrees may align better with your plans).

 

[m_s]

ECON 532 is raw, unless you do a mini research project, it's pure theory it seems like, and I an MS level.

ECON 472 seems fun, time series is all what is it you are going to use in the field of finance when it comes to Econometrics

532 would be cross sectional is seems like.