MS in Chemical Engineering with a Concentration in Data and Computational Science

The coursework for a concentration in Data and Computational Science provides the MS candidate with an understanding of data science tools and computational modeling methods relevant to modern chemical engineering practice. These tools include methods of data curation, statistical data analysis, predictive modeling, and experimental design that integrate advances in machine learning and artificial intelligence into the chemical engineering domain. Hack-a-thons planned throughout the MS program provide students with the opportunity to apply the tools they learn to solve real-world problems.
 
Students must satisfy the chemical engineering core and technical elective requirements with no more than 2 courses outside of the department. Through the S2E program, the concentration is also appropriate for students with undergraduate degrees in chemistry, materials science, mechanical engineering and related disciplines.

Any combination of four courses (12 points total) satisfies the Data and Computational Science Concentration Requirement. You can also substitute 3 credits of a Data or Computational course from across SEAS as part of this 12 point requirement.

Course Number

Title

Professor

CHEN 4011$

Numerical Methods in Chemical Engineering

Bishop

CHEN E4670*

Chemical Engineering Data Analysis

Bishop

CHEN E4580*

Artificial Intelligence in Chemical Engineering

Venkatasubramanian

CHAP E4120

Statistical Mechanics

O'Shaughnessy

CHEN E4150

Computational Fluid Dynamics in Chemical Engineering

Boyce

CHEN E4880

Atomistic Simulations for Science and Engineering

Urban

CHEN E9400

Research (Only 3 Points can be used to satisfy concentration requirement to be taken after completion of first semester.)

 

$ Required for all students in concentration

*Scientist to Engineer (S2E) students are eligible to take this elective during the first semester and count the course as within the Department of Chemical Engineering.

For more information, please email sk2794@columbia.edu