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.

Data Science (DATS) refers to the statistical analysis and interpretation of data resulting from experimental measurements or simulations. Applications of data science also go well beyond science and engineering to include business, arts, humanities, social science and more.

Typically (but not exclusively), DATS candidates may have backgrounds in a broad range of fields and are principally interested in furthering their knowledge in data science methodology.

Scientific Computing (SCMP) 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.  Example applications include the flow of fluid in complex settings, e.g., earth’s atmosphere or within the network of blood vessels in the body, the motions of large collections of atoms and molecules, or the distribution of mechanical stresses in a large, heterogeneous object such as a skyscraper.

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 Core

Technical and Depth Area Electives

DATS SCMP DATS SCMP DATS

SCMP

Programming Languages & Techniques

 

CIT 590 or     CIT 591

Programming Languages & Techniques

 

CIT 590 or       CIT 591

Mathematical Foundations

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

Mathematical Foundations

 

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

 

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 Section H  (Simulation Methods for Natural Science/Engineering)of Methods

AND

2 courses from either Section A (Application Thesis/Independent) or Section D (Natural Science/Engineering) of Applications

AND

One course from any bucket

AND

One free elective (subject to approval)

BUCKETS for Technical & Depth Area Electives

Applications

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.
  • 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)
  •  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)

Methods

  • Accelerated Regression Analysis (STAT 621)
  • Forecasting Methods for Management (STAT 711)
  • Predictive Analytics for Business (STAT 722)
  • 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
  • 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)
  • Advanced Linear Algebra (AMCS 514)
  • Algorithms (CIS 502)
  • Linear Algebra/Optimization (CIS 515)
  • Computational Learning Theory (CIS 625)
  • Randomized Algorithms (CIS 677)
  • Algorithms & Computation (CIT 596) 
  • 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)
  • Mathematical Statistics (STAT 512)
  • Stochastic Processes (STAT 533)
  • Bayesian Statistical Theory and Methods (STAT 927)