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:

  1. Foundations (two course units)
    • Programming Languages & Techniques (PL): Programming Languages & Techniques (CIT 590) or Introduction to  Software Development (CIT 591)
    • Algorithms: Algorithms & Computation (CIT 596)
  2. Core Requirements (three course units)
    • Math: Numerical Methods and Modeling (ENM 502)
    • Big Data Analytics: Big Data Analytics (CIS 545)
    • Mining and Learning: Intro to Machine Learning (CIS519) or Machine Learning (CIS 520) or Modern Data Mining (STAT 571)
  3. Technical & Depth Area Electives (five course units).
    • Students must choose two courses from Methods Bucket H (Simulation Methods for Natural Science/Engineering)
    • AND
    • Two courses from either Applications Bucket A (Thesis/Independent) or Applications Bucket D (Natural Science/Engineering)
    • AND
    • One course from any bucket
    • OR
    • One free elective (subject to approval)

BUCKETS for Technical & Depth Area Electives


A. TitleThesis/Practicum (two course units)

Register for two credits of DATS 597/Master’s Thesis or two credits of DATS 599/Master’s Independent Study. Suggestions for projects will be provided to students. Students may choose from these suggested projects or may also come up with their own project/advisor ideas. Students will be mentored jointly by the Program Director and by an advisor in the area of the project, and must receive approval by Faculty Director. 

B. Bio Medicine

Brain-Computer Interfaces (BE 521)

Network Neuroscience (BE 566)

Modeling Biological Systems (BE 567)

Bioinformatics (STAT 953)

Computational Neuroscience (PHYS 615) 

C. Social/Network Science

Econometrics (ECON 705, ECON 706, ECON 721, ECON 722)

Applied Probability Models in Marketing (MKTG 476/776)

D. Natural Science/Engineering

 Generally, any course in which the primary focus is a physical/chemical/biological/mechanical application area that may be studied computationally is allowed. Example courses include:

Chemical Engineering:

Advanced Chemical Kinetics and Reactor Design (CBE 621)

Transport Processes II (Nanoscale Transport) (CBE 641)

Interfacial Phenomena (CBE 535)

Mechanical Engineering:

Aerodynamics (MEAM 545)

Nanotribology (MEAM 537)

Micro and Nano Fluidics (MEAM 575)


Nanoscale Systems Biology (BE 555)

Fundamental Techniques of Imaging I & II (BE 546/547)

Biomedical Image Analysis (BE 537)

Materials Science and Engineering

Electronic Properties of Materials (MSE 536)

Phase Transformations (MSE 540)

Elasticity and Micromechanics of Materials (MSE 550)


E. Data-centric Programming

Software Systems (CIS 505)

Software Engineering (CIS 573)

Computer Systems Programming (CIT 595)

Advanced Programming (CIS 552)

Internet and Web Systems (CIS 555)

Programming and Problem Solving (CIS 559)

F. Data Collection, Representation, Management and Retrieval

Databases (CIS 550)

Sample Survey Methods (STAT 920)

Observational Studies (STAT 921)

G. Data Analysis, Artificial Intelligence

Computational Linguistics (CIS 530)

Computer Vision (CIS 580CIS 581)

Advanced Topics in Computer Vision (CIS 680)

Computational Learning Theory (CIS 625)

Data Mining: Learning from Massive Datasets (ESE 545)

Modern Data Mining (STAT 571)

Advanced Topics in ML (CIS 700)

Forecasting and Time-Series Analysis (STAT 910)

AIgorithms (CIS 502CIS 677CIT 596)

AI (CIS 521)

Learning in Robotics (ESE 650)

Modern Regression for the Social, Behavioral and Biological Science (STAT 974) 

H. Simulation Methods for Natural Science/Engineering

Atomic Modeling in Materials Science (MSE 561) 

Multiscale Modeling of Biosystems (BE 559) 

Molecular Modeling and Simulations (CBE 525) 

Computational Science of Energy and Chemical Transformations (CBE 544) 

Finite Element Analysis (MEAM 527) 

Computational Mechanics (MEAM 646)

I. Modelling

Simulation Modeling and Analysis (ESE 603

Control of Systems (ESE 505)

Topics In Computational Science and Engineering (ENM 540)

J. Statistics, Mathematical Foundations

Numerical Methods (ENM 502)

Linear Algebra/Optimization (CIS 515)

Complex Analysis (AMCS 510)

Introduction to Optimization Theory (ESE 504)

Regression Analysis (STAT 621)

Stochastic Processes (STAT 533)

Convex Optimization (ESE 605)

Information Theory (ESE 674)

Information re: A comparison between Scientific Computing and Data Science can be accessed here


Please visit the Penn Engineering’s Graduate Studies page for information on application materials, application deadlines, and a link to the online application.

Please visit the Graduate Student Center’s page for information on graduate student life and available resources at Penn.