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I'm graduating this year with a B.S in Computational Physics (the difference is the second semester upper division physics courses aren't required, and are replaced with a year of numerical analysis and a CS minor. But I took the 2nd semester physics back when I was a physics major anyways) and am looking to transfer to UMN.
I'm currently torn between UMN's Scientific Computing M.S and Applied Math M.S. I suspect me being torn stems from not really understanding how the career opportunities differ. I would like to work in industry preferably in a field that uses a lot of numerical analysis and mathematical modelling (I'm comfortable using languages such as Python, C++, Java, etc). I would be OK with something in finance as long as it utilized my interests. I'll describe the two programs and it would be helpful if you guys could help me understand what type of careers these two fields would prepare me for.
For the scientific computing program, it says it requires 28 course credits and 10 thesis credits. 8 of the course credits must be in scientific computing and I guess the rest can be more in scientific computing or courses in one of the supporting departments (I would pick a good amount in math).
The courses offered in scientific computing are:
SciC 8001 Parallel High-Performance Computing
SciC 8011 Scientific Visualization
SciC 8021 Advanced Numerical Methods
SciC 8031 Modeling, Optimization, and Statistics
SciC 8041 Computational Aspects of Finite Element Methods
SciC 8090 Topics in Scientific Computation
SciC 8095 Problems in Scientific Computation
SciC 8190 Supercomputer Research Seminar
SciC 8594 Scientific Computation Directed ResearchThe courses that can be picked from the math department for the scientific computing program are:
Math 5467 Introduction to Mathematics of Image and Data Analysis
Math 5485 Introduction to Numerical Methods I
Math 5486 Introduction to Numerical Methods II
Math 5487 Computational Methods for Differential and Integral Equations in Engineering and Science I
Math 5488 Computational Methods for Differential and Integral Equations in Engineering and Science II
Math 5535 Dynamical Systems and Chaos
Math 5651 Basic Theory of Probability and Statistics
Math 5705 Enumerative Combinatorics
Math 5707 Graph Theory and Non-enumerative Combinatorics B
Math 8441 Numerical Analysis and Scientific Computing I
Math 8442 Numerical Analysis and Scientific Computing II
Math 8445 Numerical Analysis and Differential Equations
Math 8450 Topics in Numerical Analysis
Math 8571 Theory of Evolutionary Equations
As for the Math M.S, it requires 20 course credits, 14 in math and 6 in another field (another field as in CSE, Scientific Computing, etc) and then 10 thesis credits.
Anything can be chosen from the math program including pure math classes not listed above but I would pick applied math courses.
So my question is, what is the differences in career opportunities in these two fields? Do they both have a good chance in financial modelling or do employers prefer one over the other? Do jobs in these fields usually require a PhD? I'd be interested in hearing about any careers that use these types of skills.
I'm currently torn between UMN's Scientific Computing M.S and Applied Math M.S. I suspect me being torn stems from not really understanding how the career opportunities differ. I would like to work in industry preferably in a field that uses a lot of numerical analysis and mathematical modelling (I'm comfortable using languages such as Python, C++, Java, etc). I would be OK with something in finance as long as it utilized my interests. I'll describe the two programs and it would be helpful if you guys could help me understand what type of careers these two fields would prepare me for.
For the scientific computing program, it says it requires 28 course credits and 10 thesis credits. 8 of the course credits must be in scientific computing and I guess the rest can be more in scientific computing or courses in one of the supporting departments (I would pick a good amount in math).
The courses offered in scientific computing are:
SciC 8001 Parallel High-Performance Computing
SciC 8011 Scientific Visualization
SciC 8021 Advanced Numerical Methods
SciC 8031 Modeling, Optimization, and Statistics
SciC 8041 Computational Aspects of Finite Element Methods
SciC 8090 Topics in Scientific Computation
SciC 8095 Problems in Scientific Computation
SciC 8190 Supercomputer Research Seminar
SciC 8594 Scientific Computation Directed ResearchThe courses that can be picked from the math department for the scientific computing program are:
Math 5467 Introduction to Mathematics of Image and Data Analysis
Math 5485 Introduction to Numerical Methods I
Math 5486 Introduction to Numerical Methods II
Math 5487 Computational Methods for Differential and Integral Equations in Engineering and Science I
Math 5488 Computational Methods for Differential and Integral Equations in Engineering and Science II
Math 5535 Dynamical Systems and Chaos
Math 5651 Basic Theory of Probability and Statistics
Math 5705 Enumerative Combinatorics
Math 5707 Graph Theory and Non-enumerative Combinatorics B
Math 8441 Numerical Analysis and Scientific Computing I
Math 8442 Numerical Analysis and Scientific Computing II
Math 8445 Numerical Analysis and Differential Equations
Math 8450 Topics in Numerical Analysis
Math 8571 Theory of Evolutionary Equations
As for the Math M.S, it requires 20 course credits, 14 in math and 6 in another field (another field as in CSE, Scientific Computing, etc) and then 10 thesis credits.
Anything can be chosen from the math program including pure math classes not listed above but I would pick applied math courses.
So my question is, what is the differences in career opportunities in these two fields? Do they both have a good chance in financial modelling or do employers prefer one over the other? Do jobs in these fields usually require a PhD? I'd be interested in hearing about any careers that use these types of skills.