How does a Master's in Mathematics/Statistics sound for my case?
Context:
I have a Bachelor's in Electrical Engineering and a Master's in Computer Science. As you would expect, my Bachelor's in EE had a strong high-level mathematics component to it, but that was 3-4 years ago. My Master's inCShad little/no math. Right now I'm about to join a Bulge Bracket Investment Bank with Rates Electronic Trading team. I'm not sure exactly what sort of development I'll be doing and if it's directly related to the trading platform.
Case:
I'm looking to pivot myself toward a quantitative developer role in the near future. Specifically with hedge funds/buy-side firms likeCitadel/Two Sigma,Jump Tradingetc. Note that I'm not considering quantitative researcher or quantitative trader roles, just purely quantitative developer as I enjoy and want to work with software primarily but in the financial space. Now I'm very well aware that QDs will have to have a decent/strong mathematics background to be able to understand and communicate effectively with QRs and QTs and implement their solutions.
所以given my background, do you think a second Master's in Mathematics/Statistics would solidify me for that path or is it just a complete waste of time/money? I could do the Master's in 1 year full-time or 2 years part-time. Or should I take online courses/refreshers?
Pure Mathematics electives:
- Algebra 3
- Algebra 4
- Algebraic Curves
- Algebraic Geometry
- Algebraic Number Theory
- Algebraic Topology
- Bifurcation Theory
- Classical Dynamics
- Commutative Algebra
- Complex Manifolds
- Differential Topology
- Dynamical Systems
- Dynamics of Learning and Iterated Games
- Elliptic Curves
- Formalising Mathematics
- Fourier Analysis and Theory of Distributions
- Functional Analysis
- Galois Theory
- Geometric Mechanics
- Geometry of Curves and Surfaces
- Graph Theory
- Group Representation Theory
- Group Theory
- Lie Algebras
- Manifolds
- Markov Processes
- Number Theory
- Probability Theory
- Random Dynamical Systems and Ergodic Theory: Seminar Course
- Riemannian Geometry
- Stochastic Calculus with Applications to non-Linear Filtering
Statistics:
- Probability for Statistics
- Fundamentals of Statistical Inference
- Applied Statistics
- Computational Statistics
- 统计数据研究项目
- Introduction to Statistical Finance
- Advanced Statistical Finance
- Stochastic Processes
- Contemporary Statistical Theory
- Bayesian Methods
- Multivariate Analysis
- Machine Learning
- Biomedical Statistics
- Statistical Genetics and Bioinformatics
- Big Data
- Advanced Simulation Methods
- Data Science
- Deep Learning with TensorFlow
- Nonparametric Statistics
- Time Series Analysis
- Survival Models
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