Scientific Computing and Data Science: A Comparison

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

Data Science Scientific Computing
Foundation Courses (2 CU)

Programming Languages & Techniques

CIT 590 or CIT 591

and

Algorithms

CIT 596

Foundation Courses (2 CU)

Programming Languages & Techniques

CIT 590 or CIT 591

and

Algorithms

CIT 596

Core Requirements (3 CU)

Math

CIS 515 or STAT 512 or ESE542

and

Big Data Analytics

CIS 545

and

Mining and Learning

CIS 519 or CIS 520 or STAT 571 or ENM 531 or ESE 545

Core Requirements (3 CU)

Math

ENM 502

and

Big Data Analytics

CIS 545

and

Mining and Learning

CIS 519 or CIS 520 or STAT 571 or ENM 531 or ESE 545

Technical and Depth Area Electives (5 CU)

Students must choose courses from at least 3 of the buckets listed below. Two courses must represent a depth sequence, which can be the thesis/practicum or two courses which build on each other (e.g. one is a prerequisite of the other).

Technical and Depth Area Electives (5 CU)

Students must choose:

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

and

2 courses from either Bucket A (Thesis/Independent) or Bucket C (Applications in Natural Science/Engineering)

and

1 free elective (subject to approval).

Program Buckets

DATS Buckets

Technical and Depth Area Electives (5 CU)

  1. Thesis/Practicum Option. Two CU of DATS 597 or DATS 599
  2. Biomedicine
    • Brain-Computer Interfaces (BE 521)
    • Network Neuroscience (BE 566)
    • Mathematical Computation Methods for Modeling Biological Systems (BE 567)
    • Introduction to Computational Biology and Biological Modeling (CIS 536)
    • Biomedical Image Analysis (CIS 537)
    • Theoretical and Computational Neuroscience (PHYS 585)
    • Bioinformatics (STAT 953)
  3. Social/Network Science
    • Econometrics I- Fundamentals (ECON 705)
    • Econometrics III: Advanced Techniques of Cross-Section Econometrics (ECON 721)
    • Econometrics IV: Advanced Techniques of Time-Series Econometrics (ECON 722)
    • Applied Probability Models in Marketing (MKTG 776)
  4. Data-Centric Programming
  5. Surveys and Statistical Methods
    • Forecasting and Time-Series Analysis (STAT 910)
    • Sample Survey Methods (STAT 920)
    • Observational Studies (STAT 921)
    • Modern Regression for the Social, Behavioral and Biological Science (STAT 974)
    • Accelerated Regression Analysis (STAT 621)
  6. Data Analysis, Artificial Intelligence
    • Artificial Intelligence (CIS 521)
    • Deep Learning for Data Science (CIS 522)Computational Linguistics (CIS 530)
    • Machine Perception (CIS 580)
    • Computer Vision (CIS 581)
    • Advanced Topics in ML (CIS 620)
    • Advanced Topics in Computer Vision (CIS 680)
    • Learning in Robotics (ESE 650)
    • Modern Data Mining (STAT 571)
  7. Simulation Methods for Natural Science/Engineering
    • Molecular Modeling and Simulations (CBE 525)
    • Computational Science of Energy and Chemical Transformations (CBE 544)
    • Finite Element Analysis (MEAM 527)
    • Computational Mechanics (MEAM 646)
    • Atomic Modeling in Materials Science (MSE 561)
    • Multiscale Modeling of Biological Systems (BE 599)
    • Mathematical Computation Methods for Modeling Biological Systems (BE 567)
  8. Mathematical and Algorithmic Foundations
    • Advanced Linear Algebra (AMCS 514)
    • Algorithms (CIS 502)
    • Linear Algebra/Optimization (CIS 515)
    • Computational Learning Theory (CIS 625)
    • Randomized Algorithms (CIS 677)
    • Numerical Methods (ENM 502)
    • Data-driven Modeling and Probabilistic Scientific Computing (ENM531)
    • Introduction to Optimization Theory (ESE 504)
    • Data Mining: Learning from Massive Datasets (ESE 545)
    • Simulation Modeling and Analysis (ESE 603)
    • Convex Optimization (ESE 605)
    • Information Theory (ESE 674)
    • Stochastic Processes (STAT 533)

SCMP Buckets

Technical and Depth Area Electives (5 CU)

  1. Thesis/Practicum Option
    • Two CU of SCMP 597 or SCMP 599
  2. Simulation Methods for Natural Science/Engineering
    • Molecular Modeling and Simulations (CBE 525)
    • Computational Science of Energy and Chemical Transformations (CBE 544)
    • Finite Element Analysis (MEAM 527)
    • Computational Mechanics (MEAM 646)
    • Atomic Modeling in Materials Science (MSE 561)
    • Multiscale Modeling of Biological Systems (BE 599)
    • Mathematical Computation Methods for Modeling Biological Systems (BE 567)
  3. Applications in Natural Science/Engineering
    • Any course in which the primary focus is a physical/chemical/biological/mechanical application area that may be studied computationally is allowed, subject to approval.
Graduate Program:

Your CIS Contacts:

Mike Felker – CIS Graduate Admissions
Graduate Coordinator for on-campus Master in Computer and Information Technology and Master of Science in Engineering 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

Joy McManus
Graduate Coordinator for ROBO
Office: 459 Levine
Phone: 215-573-4907
Email: joymc@seas.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