## 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
Algorithms CIT 596 |
Foundation Courses (2 CU)Programming Languages & Techniques CIT 590 or CIT 591
Algorithms CIT 596 |

Core Requirements (3 CU)Math CIS 515 or STAT 512 or ESE542
Big Data Analytics CIS 545
Mining and Learning CIS 519 or CIS 520 or STAT 571 or ENM 531 or ESE 545 |
Core Requirements (3 CU)Math ENM 502
Big Data Analytics CIS 545
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 (
2 courses from either Bucket A (
1 free elective (subject to approval). |

**Program Buckets**

### DATS Buckets

__Technical and Depth Area Electives (5 CU)__

**Thesis/Practicum Option.**Two CU of DATS 597 or DATS 599**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)

**Social/Network Science****Data-Centric Programming****Surveys and Statistical Methods****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)

**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)

**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)__

**Thesis/Practicum Option**- Two CU of SCMP 597 or SCMP 599

**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)

**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:

**Redian Furxhiu****
**Graduate Coordinator for on-campus MCIT, CIS/MSE and CGGT programs

Office: 308 Levine

Phone: 215-898-1668

Email: redian@seas.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: robo-coord@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