CMSE Graduate Courses

The Department of Computational Mathematics, Science and Engineering currently offers several courses pertaining to computational and data science, as described below. We anticipate that a significant number of additional courses will be added each year - check the Special Courses page or the course catalog for more information!

Please contact the CMSE Graduate Director for additional information and updates.

CMSE Graduate Courses

CMSE 801, Introduction to Computational Modeling.  Introduction to computational modeling using a wide variety of application examples. Algorithmic thinking and model building, data visualization, numerical methods, all implemented as programs. Command line interfaces. Scientific software development techniques including modular programming, testing, and version control.  Recommended background: one semester of introductory calculus.  (3 credits)  Offered every fall and spring semester, starting in Spring 2016. 

CMSE 802, Methods in Computational Modeling.  Standard computational modeling methods and tools. Programming and code-management techniques.  Recommended background:  CMSE 801 or equivalent experience.  (3 credits)  Offered every fall and spring semester, starting in Fall 2016.

CMSE 820, Mathematical Foundations of Data Science. Introduces students to the fundamental mathematical principles of data science that underlie the algorithms, processes, methods, and data-centric thinking. Introduces students to algorithms and tools based on these principles.  Recommended background:  CMSE 802 or equivalent experience.  Differential equations at the level of MTH 235/255H/340+442/347H+442.  Linear algebra at the level of MTH 390/317H.  Probability and statistics at the level of STT 231.  (3 credits)  Offered every spring semester, starting in Spring 2017.

CMSE 821, Numerical Methods for Differential Equations. Numerical solution of ordinary and partial differential equations, including hyperbolic, parabolic, and elliptic equations. Explicit and implicit solutions. Numerical stability.   Recommended background:  CMSE 802 or equivalent experience.  Differential equations at the level of MTH 235/255H/340+442/347H+442.  Linear algebra at the level of MTH 390/317H.  (3 credits)  Offered every spring semester, starting in Spring 2017.

CMSE/CSE 822, Parallel Computing.  Core principles and techniques of parallel computation using modern supercomputers. Parallel architectures. Parallel programming models. Principles of parallel algorithm design. Performance analysis and optimization. Use of parallel computers.  Recommended background: One semester of introductory calculus. Ability to program proficiently in C/C++, basic understanding of data structures and algorithms (both at the level of CSE 232). Basic linear algebra and differential equations.  (3 credits)  Offered every fall semester, starting in Fall 2016.

CMSE 823, Numerical Linear Algebra, I.  Convergence and error analysis of numerical methods in applied mathematics.  Recommended background: CMSE 802 or equivalent experience; Linear algebra at the level of MTH 414. (3 credits)   Offered every fall semester, starting in Fall 2016.

CMSE 890, Selected Topics in Computational Mathematics, Science, and Engineering.  Topics selected to supplement and enrich existing courses and lead to the development of new courses.  Recommended background varies with topic and instructor.  (1-4 credits)  Note: A student may earn a maximum of 12 credits in all enrollments fog this course.  Offerings vary; consult the Special Courses  or Registrar's website.

CMSE 891, Independent Study in Computational Mathematics, Science, and Engineering.  (1-4 credits)  Enrollment by approval only.

CMSE 899, Master's Thesis Research. Master's thesis research.  (1-6 credits)  Note: A student may earn a maximum of 8 credits in all enrollments for this course.  Enrollment by approval only.

CMSE 999, Doctoral Dissertation Research.  Doctoral dissertation research.  (1-24 credits) Note: A student may earn a maximum of 36 credits in all enrollments for this course.  Enrollment by approval only.