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.
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.
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 dualdegree 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

Programming Languages & Techniques

Mathematical Foundations
STAT 512 or CIS 515 or
AND
Big Data Analytics
AND 
Mathematical Foundations
AND
Big Data Analytics
CIS 545 or
AND
Mining and Learning
CIS 519 or CIS 520 or ENM 531 or ESE 545 or

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 BI) 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
 BrainComputer 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)
Methods
 Accelerated Regression Analysis (STAT 621)
 Forecasting Methods for Management (STAT 711)
 Predictive Analytics for Business (STAT 722)
 Forecasting and TimeSeries Analysis (STAT 910)
 Sample Survey Methods (STAT 920)
 Observational Studies (STAT 921)
 Modern Regression for the Social, Behavioral and Biological Science (STAT 974)
 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)
 Principles of Deep Learning (ESE 546)
 Learning in Robotics (ESE 650)
 Modern Data Mining (STAT 571)
 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)
 Datadriven 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)