Scientific Computing  program aims to produce highly skilled scientist capable of applying numerical methods and critical evaluation of their results to their field in science and engineering. It brings together best practice in computing with cutting edge science and fills in the computational gap in the traditional mathematical, science, and engineering programs. Therefore, the research areas in the program range from foundation mathematics and fundamental numerical algorithms to such practical topics in computational fluid dynamics, PDE-constrained optimization, model order reduction, statistical learning, computational biology, high performance computing, uncertainty quantification, etc.  So recommended elective courses can be categorized in the following specialized areas:

• Computational Finance (also known as Financial Engineering) is a cross-disciplinary field; and comprises of subjects on mathematical finance, numerical methods, and computer simulations. Utilizing various methods of applied mathematics (and engineering), practitioners of computational finance aim to value financial instruments (financial derivatives, options, etc.), create optimal portfolios of such instruments, and determine risks of such portfolios (of financial instruments).
• Computational Mathematics and Simulation comprises the rigorous mathematical analysis of numerical methods, often concerned with issues of discretization, approximation and convergence, the development and analysis of numerical algorithms, the implementation of these algorithms on modern computer architectures, and the use of numerical methods in conjunction with mathematical modeling to solve large-scale practical problems.  Particular research foci are numerical methods for partial differential equations (finite elements, adaptive finite element methods), numerical linear algebra, PDE-constrained optimization, model order reduction techniques, and algorithms for uncertainty quantification.
• Data Science is a multidisciplinary field that uses statistics, mathematics, and computer science in order to extract significant information from various forms of data. Over the last decade it has become a very crucial topic for many industries: finance, insurance, healthcare, energy, web analytics, cybersecurity are some of them among many others.
• Mathematical Modelling and Applications, a specialized area, is designed for students interested in the skills and knowledge required to develop efficient and robust numerical solutions to engineering  problems such as  heart electromechanics, flows in porous media, fluid dynamics, structural analysis, mass transport, heat transfer and, more in general, to multiscale and multiphysics applications. 
• Operations Research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. Analytical methods used in OR include mathematical logic, simulation, network analysis, queuing theory, and game theory. Some Techniques of Operations Research include: linear programming, transportation problem, assignment problem, queuing theory, game theory, inventory control models, and goal programming.

The list of possible elective courses is not limited to the list below; and the list is expanded on a continuing basis; courses not in the list can be accepted as elective subject to the approval of the student's adviser.