University courses/path to becoming a QUANT
Hello everyone,
My current background is in applied mathematics and physics. I have several top publications as the first author in some domain of theoretical physics. I have a weak programming background, don't know optimization methods, little knowledge of artificial intelligence methods (machine learning, deep learning).
In 2 years from now, I would love to join one of the funds to work as a quantitative researcher. I am here for hearing advise from more experienced people working as quants or knowing something I haven't taken into account yet about correctness of my path.
To accomplish my goal, I have joined 2 years master degree program in data science with a very flexible curriculum, see http://msc.skoltech.ru/data-science (MLAI track) and the total list of courses available to be taken https://www.skoltech.ru/en/education/course-catal…. I have created my own curriculum as follows. (Term 1A lasts 4 weeks, each of Terms 2,3,4 lasts 8 weeks):
Term 1A (starts in October): Efficient algorithms and Data structures part 1 (3 ECTS); Introduction to Data science (3 ECTS); Mathematical methods in engineering and applied science part 1 (3 ECTS)
Term 2: Efficient algorithms and Data structures part 2 (3 ECTS); Numerical linear algebra (6 ECTS);
Mathematical methods in engineering and applied science part 2 (3 ECTS); Convex optimization and applications (3 ECTS)
Term 3: Machine learning (6 ECTS); Advanced statistical methods (3 ECTS); Numerical methods in engineering and science (6 ECTS), Optimization methods (6 ECTS)
Term 4: Deep Learning (6 ECTS); Stochastic methods in mathematical modelling (6 ECTS); Geometrical methods of machine learning (3 ECTS)
The programming in the courses will primarily be with Python, sometimes Matlab and C++.
The program lasts 2 years, so I have also Terms 5-8. The courses can also be taken/distributed during the second year of study. I will also need to write a master thesis - I have chosen to perform original research in statistical machine learning domain.
Is there any skill necessary for my success that the courses mentioned above won't cover?
Hey GoBig, I'm here to break the silence...any of these links help you?:
I hope those threads give you a bit more insight.
Take some courses on probability. Seems a bit too heavy on the machine learning. Deep learning is very unlikely to be used at a quant fund, and for the ML classes you want to take more of a statistical rather than engineering approach.
Thank you for your reply. I got your point. What classes other than on probability do you think can be useful for me? I mean I can substitute some classes, e.g. deep learning, to something else, e.g. high performance computing, so I could be good as a quantitative researcher in the future and would be able to pass the interview. My main goal is to maximize my chances to pass the interview in top places.
Optimization and classical ML is good too, deep learning is just rarely used by hedge funds outside of some niche CV/NLP applications.
Do you think courses on Computer Vision and Natural Language Processing can be helpful for passing the interview and subsequent work?
Make sure you get a good grasp of linear algebra. Algorithms and data structures are used heavily too to get those backtests to run faster. Time Series math is usually relevant. Deep learning/machine learning are probably extra, though they may be used more in the future
Thank you for finding time to read my post and reply. Not sure if we have something on Time Series. What about Natural Language Processing? Can it be useful too? I see it to be valuable to predicting future price movements based on the text information
I also would like to ask: Do you think I should concentrate more on Python (I use it for quite long time already) or pick up some C++ too (low level at the moment)? I mean does it make sense to increase skills with C++ or is it possible to get a position as a quantitative researcher without C++, but knowing Python?
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