The Mathematical & Computational Finance (MCF) track is an interdisciplinary program that provides education in applied and computational mathematics, statistics, and financial applications for individuals with strong mathematical skills. Upon successful completion of the MCF track in the ICME M.S. program, students will be prepared to assume positions in the financial industry as data and information scientists, quantitative strategists, risk managers, regulators, financial technologists, or to continue on to their Ph.D. in ICME, MS&E, Mathematics, Statistics, Finance and other disciplines.
The Institute for Computational and Mathematical Engineering, in close cooperation with Mathematics, Management Science and Engineering and Statistics provide many of the basic courses.
Note: This new track in the ICME M.S. Program will supersede, beginning in the fall quarter of 2014, the interdisciplinary M.S. Program (IDP) in Financial Mathematics in the School of Humanities & Sciences.
Students must demonstrate foundational knowledge in the field by completing the following core courses. Courses in this area must be taken for letter grades. Deviations from the core curriculum must be justified in writing and approved by the student’s ICME adviser and the chair of the ICME curriculum committee. Courses that are waived may not be counted towards the master’s degree.
Units 


The following courses are required: 

Numerical Linear Algebra 
3 

Numerical Optimization ^{1} 
3 

or CME 364A 
Convex Optimization I 

Stochastic Methods in Engineering (or an equivalent course approved by the committee) ^{2} 
3 

or MATH 236 
Introduction to Stochastic Differential Equations 

Total Units 
9 
Data Science electives should demonstrate breadth of knowledge in the technical area. The elective course list is defined. Courses outside this list can be accepted as electives subject to approval. Petitions for approval should be submitted to student services.
Units 


Take 9 units of the following: 

Statistical Methods in Finance 
34 

Econometric Modeling Methodology and Applications to Financial Markets 
34 

Financial Models and Statistical Methods in Active Risk Management 
34 

Modern Applied Statistics: Learning 
23 

Modern Applied Statistics: Data Mining 
23 
Choose three courses in specialized areas from the following list. Courses outside this list can be accepted as electives subject to approval. Petitions for approval should be submitted to student services.
Units 


Take three or four of the following: 

Debt Markets 
4 

Financial Markets I 
3 

Financial Markets II 
4 

Dynamic Asset Pricing Theory 
4 

Default and Systemic Risk 
3 

Computation and Simulation in Finance 
3 

Mathematical Finance 
3 

Credit Risk: Modeling and Management 
3 

Optimization of Uncertainty and Applications in Finance 
3 

Algorithmic Trading and Quantitative Strategies 
3 
To ensure that students have a strong foundation in programming students are required to take 6 units of advanced programming for letter grade, with at least 3 units in parallel computing. Approved courses for advanced scientific programming include:
Units 


Advanced Scientific Programming; take 36 units 

Advanced Programming for Scientists and Engineers 
3 

Software Design in Modern Fortran for Scientists and Engineers 
3 

Computer Organization and Systems 
35 

Largescale Software Development 
3 

Parallel/HPC Computing (at least 3 units required) 

Introduction to parallel computing using MPI, openMP, and CUDA 
3 

Parallel Methods in Numerical Analysis 
3 

Parallel Computing 
34 

Parallel Computer Architecture and Programming 
3 

Advanced MultiCore Systems 
3 

CS 344C, offered in previous years, may also be counted 
3 
Students are required to take 6 units of practical component that may include any combination of:
Units 


Project Course in Mathematical and Computational Finance 
16 

Topics in Mathematical and Computational Finance 
1 

Projects in Wealth Management 
34 