Data Science, MSE

Programming Languages and Techniques
Introduction to Software Development
Introduction to Probability and Statistics
Probability
Advanced Probability
Mathematical Statistics
Fundamentals of Linear Algebra and Optimization
Computational Learning Theory
Big Data Analytics
Applied Machine Learning
Machine Learning
Modern Data Mining

The ten course units for the Data Science degree are divided into three categories: Foundations, Core Requirements and Technical & Depth Area electives. (As long as the prerequisites for the courses are met, students can complete these courses in any sequence)

In lieu of these courses, students may take Technical Electives and are encouraged (but not required) to take a course from Bucket C in lieu of Probability, and a course from Bucket B in lieu of PL.

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-J) two courses, one of which builds on the other (e.g. is a prerequisite).

Brain-Computer Interfaces
Network Neuroscience
Mathematical Computation Methods for Modeling Biological Systems
Econometrics I: Fundamentals
Econometrics II: Methods & Models
Econometrics III: Advanced Techniques of Cross-Section Econometrics
Econometrics IV: Advanced Techniques of Time-Series Econometrics
Applied Probability Models for Marketing
Advanced Chemical Kinetics and Reactor Design
Transport Processes II (Nanoscale Transport)
Interfacial Phenomena
Aerodynamics
Nanotribology
Micro and Nano Fluidics
Nanoscale Systems Biology
Fundamental Techniques of Imaging I
Biomedical Image Analysis
Nanotribology
Phase Transformations
Elasticity and Micromechanics of Materials
Software Systems
Software Engineering
Computer Systems Programming
Advanced Programming
Internet and Web Systems
Programming and Problem Solving
Database and Information Systems
Sample Survey Methods
Observational Studies
Computational Linguistics
Machine Perception
Computer Vision & Computational Photography
Advanced Topics in Machine Perception
Computational Learning Theory
Data Mining: Learning from Massive Datasets
Modern Data Mining
Analysis of Algorithms
Artificial Intelligence
Advanced Topics in Algorithms and Complexity
Special Topics
Algorithms and Computation
Learning in Robotics
Modern Regression for the Social, Behavioral and Biological Sciences
Multiscale Modeling of Chemical Systems
Molecular Modeling and Simulations
Computational Science of Energy and Chemical Transformations
Finite Element Analysis
Computational Mechanics
Atomic Modeling in Materials Science
Topics In Computational Science and Engineering
Feedback Control Design and Analysis
Complex Analysis
Fundamentals of Linear Algebra and Optimization
Numerical Methods and Modeling
Intro to Linear, Nonlinear and Integer Optimization
Modern Convex Optimization
Information Theory
Stochastic Processes
Accelerated Regression Analysis for Business

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-J) two courses, one of which builds on the other (e.g. is a prerequisite).

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.

Generally, any course in which the primary focus is a physical/chemical/biological/mechanical application area that may be studied computationally is allowed.