Your CIS Department Contacts:

Mike Felker - CIS Graduate Admissions
Graduate Coordinator for on-campus Master in Computer and Information Technology and Master of Science in Engieering programs
Office: 158 Levine
Phone: 215-898-9672
Email: mfelker@cis.upenn.edu

Britton Carnevali
Graduate Coordinator for PhD program
Office: 310 Levine
Phone: 215-898-5515
Email: brittonc@cis.upenn.edu

Charity Payne
Graduate Coordinator for ROBO
Office: 459 Levine
Phone: 215-898-0374
Email: charity@cis.upenn.edu

Liz Wai-Ping Ng
Associate Director for Embedded Systems MSE program
Office: 313 Levine
Phone: 215-898-8543
Email: wng@cis.upenn.edu

Attention US MIlitary Veterans and Active Duty Service Members:

The application fee to apply for a CIS graduate program is waived for United States active duty service members and military veterans. More information is available on the application information page. The University of Pennsylvania is a Yellow Ribbon School. Also take a look at the Veterans@Penn website.

J.P. Eckert Fellowship:

The J.P. Eckert Fellowship will provide five new master's students who are U.S. citizens or U.S. permanent residents $10,000 towards tuition & fees. Click here for more information.

Scientific Computing and Data Science: A Comparison

All Degrees Offered

Scientific Computing broadly describes the application of computer simulations, usually based on a combination of mathematical models and numerical methods, to solve problems in science and engineering. By contrast, Data Science refers to the statistical analysis and interpretation of data resulting from experimental measurements or simulations. Continued increases in available computational power have made feasible progressively larger-scale scientific simulations that produce ever-increasing quantities of data. In some cases, the processing and interpretation of this data has become a bottleneck limiting the usefulness of the simulations. Concurrently, the increasing ability to record and store data streams in many settings, e.g., weather monitoring stations, retail environments, online website traffic, etc., has pushed to the forefront the need for efficient data science tools.  These developments have created new opportunities at the nexus of the traditionally separate arenas of scientific computing and data science.

Two new Masters level programs, the Scientific Computing Master’s Program (SCMP) and the Data Science Master’s Program (DATS) have been developed within the School of Engineering and Applied Science to address this evolving landscape and to provide training for students interested in a broad range of careers. These two program share a common core consisting of courses in mathematical foundations, computer programming, machine learning, and data analytics. However, they also are distinct in important ways: the SCMP program emphasizes application of modern computer simulation methods in fields related to natural science and engineering, while DATS focuses on statistical methods in a broad range of application domains. The degree requirements for both programs are summarized below.

Typically (but not exclusively), SCMP candidates have a background in a natural science or engineering discipline, interest or prior experience with scientific computing, and seek to learn how to deploy and use data science and machine learning techniques

In both cases, some prior programming experience is useful but not absolutely required. Students who have a weak programming background but are interested in SCMP or DATS, and who wish to have a strong technical background upon graduation, are encouraged apply to the Master’s of Computer and Information Technology (MCIT) program and apply for a dual-degree with SCMP/DATS after the first semester. The MCIT program is designed for students with no prior experience in computer science. Students in this program learn programming, discrete math, data structures and algorithms, computer architecture, and software engineering, along with a number of other electives in computer science and engineering.  Graduates of this program are well positioned for a variety of software engineering and project management jobs in the tech industry.

Prospective students are encouraged to decide which of these two programs, SCMP or DATS, is the best match for their interests and apply to at most one.

DATS and SCMP Program of Study: An overview

Foundations

DATS

SCMP

Programming Languages & Techniques
CIT 590 or CIT 591

AND


Probability

ENM 503 or STAT 510 or MATH 546

Programming Languages & Techniques
CIT 590 or CIT 591

AND

Algorithms
CIT 596

   
Core  
DATS SCMP

Math
STAT 512 or 
CIS 515  or 
CIS 625

AND

Big Data Analytics

CIS 545

AND

Mining and Learning
CIS 519 or 
CIS 520 or 
STAT 571   

Math
ENM 502

AND

Big Data Analytics
CIS 545 or 
CIS 550

AND


Mining and Learning
CIS 519 or
CIS 520 or
ENM 531 or
ESE 545 or
STAT 571

Tech Electives  
DATS SCMP

 

Students must choose:

Courses from 3 different buckets, one
bucket of which can be a 2 semester
sequence of thesis/practicum. Two
of the courses must represent a depth sequence, which could be the
thesis/practicum or (for bucket options B-I)
two courses, one of which builds on the
other (e.g. is a prerequisite).

 

Students must choose:

2 courses from Methods Bucket H (Simulation Methods for Natural Science/Engineering)

AND

2 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

Applications

A. Thesis/Independent Study

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 705ECON 706ECON 721ECON 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:

1. Advanced Chemical Kinetics and Reactor Design  (CBE 621)

2. Transport Processes II (Nanoscale Transport)  (CBE 641)  

3. Interfacial Phenomena (CBE 535)  

  • Mechanical Engineering:

1.  Aerodynamics  (MEAM 545)   

2. Nanotribology  (MEAM 537)  

3. Micro and Nano Fluidics  (MEAM 575)  

  • Bioengineering:

1. Nanoscale Systems Biology  (BE 555)  

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

3. Biomedical Image Analysis  (BE 537)  

  • Materials Science and Engineering

1.  Electronic Properties of Materials  (MSE 536)  

2.  Phase Transformations  (MSE 540)  

3.  Elasticity and Micromechanics of Materials (MSE 550)

 

Methods

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, Artifical 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)

    Other courses focusing on simulation methods for 
    physical/chemical/mechanical/biological systems at the molecular, meso, 
    and/or continuum scales may also be acceptable.  

 

I. Modeling

• 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)
• Bayesian Methods (STAT 542 )
• Convex Optimization (ESE 605)
• Information Theory (ESE 674)