The MSE in Scientific Computing (SCMP) program at Penn provides multifaceted education in the fundamentals and applications of computational science. This education program provides a rigorous computational foundation for applications to a broad range of scientific disciplines. An education in SCMP combines a comprehensive set of core courses centered on numerical methods, algorithm development for high performance computational platforms, and the analysis of large data, and offers flexibility to specialize in different computational science application areas. Students may elect to pursue a thesis in computationally-oriented research within the School of Engineering and Applied Science.
We welcome applications from candidates who have a strong background in physical or theoretical sciences, engineering, math, or computer science. Some experience with computer programming is also strongly recommended.
Program of Study
The ten course units for the Scientific Computing degree are divided into three categories:
- Foundation Courses (two course units)
- Programming Languages & Techniques (CIT 5900), and
- Algorithms & Computation (CIT 5960)
- Core Requirements (three course units)
- Math: Numerical Methods and Modeling (ENM 5020) and
- Big Data Analytics (CIS 5450), and
- Mining and Learning: Applied Machine Learning (CIS 5190) or Machine Learning (CIS 5200) or Modern Data Mining (STAT 5710), or Data-driven Modeling and Probabilistic Scientific Computing (ENM 5310), or Data Mining: Learning from Massive Datasets (ESE 5450)
- Technical & Depth Area Electives (five course units).
- Two courses in Simulation Methods for Natural Science/Engineering. Examples include: ESE 5030, MSE 5610, BE 5590, CBE 5250, CBE 5440, MEAM 5270, MEAM 6460, ENM5310, ENM5400, ENM5220, CIS5800, CIS5810, BE5370/CIS5370, CIS5350/GCB5350/MTR5350/PHRM 5350, CIS5360/BIOL5360/GCB5360, CIS5370/BE5370, AMCS5680/BIOL560, advisor approval required.
- Two courses either Thesis/Independent study or Applications in Natural Science/Engineering, advisor approval required.
- One free elective, advisor approval required.
If you have any questions regarding SCMP, please email Katie Thompson at email@example.com.
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