Best Courses to take for MS Statistics (BuySide Interest)

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jsmith__

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Hi All,

I am currently enrolled in a MS Statistics program and I have the option to take three electives. I'm interested in joining the buyside and working as a quant when I graduate. For my electives, I know Applied Time Series Analysis is a must so I will go with that. Not sure what to take for my other two classes. Some options I were thinking could be Applied Econometrics I & II, Applied Bayesian Analysis, Applied Multivariate and Longitudinal Data Analysis, Neural Networks and Deep Learning. Thanks!
 
Bayesian pops up a lot, some buy side firms claim to build their methods around it. That would be a good one. Time series is needed, yes; ideally you cover through ARCH/GARCH methods. Applied Multivariate might be the most useful of the ones remaining, but NN and DL are hot topics and could be interesting.
I assume you have at least one course in computational methods already, if not then that needs to be added to the list.

Edit: Listen to Mr. Abbott over me.
 
Last edited:
jsmith__ said:
Hi All,

I am currently enrolled in a MS Statistics program and I have the option to take three electives. I'm interested in joining the buyside and working as a quant when I graduate. For my electives, I know Applied Time Series Analysis is a must so I will go with that. Not sure what to take for my other two classes. Some options I were thinking could be Applied Econometrics I & II, Applied Bayesian Analysis, Applied Multivariate and Longitudinal Data Analysis, Neural Networks and Deep Learning. Thanks!
Click to expand...
Consider optimization: LP, QP, NLP.
 
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A of Machine Learning, calibration etc. demands a good knowledge of optimisation.

Nothing takes place in the world whose meaning is not that of some maximum or minimum.

- Leonhard Euler
 
Just to hop on the thread, is Probabilistic Modeling a valuable skill in the QR sector? Topics like Generalized Linear Models, Exponential Family, Bayesian Nonparametric etc? Wondering if I shall take the elective in Probabilistic Modeling and Machine Learning, or just stick with the Time Series Analysis.
 
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