Mission

The mission of the Computational/ Data Science Master program is to train graduates that can use, advance and create new computational tools and models for understanding natural phenomena and to use such knowledge to engineer solutions that can address relevant problems in science, engineering and other related
areas. The program aims to provide students with training and research experiences that will provide them with the knowledge, skills and expertise necessary to use computational models and tools in advancing knowledge in science and/or engineering.

Program Learning Outcomes (PLOs)

The graduates of the Computational Science master degree program must be able to:

1. Frame a problem statement that requires computational tools to solve
2. Compare, critically evaluate and choose from existing computational approaches or models, or develop new computational approaches or models for solving problems in physical, biological or engineering systems
3. Formulate solutions within the limitations of available computational resources and data to develop and validate computational models
4. Synthesize and assimilate data for improving the accuracy of computational models and the performance of computational methods
5. Assess the accuracy, robustness and efficiency of numerical approaches and algorithms
6. Use existing software and computational tools to conduct research and advance knowledge in an area of inquiry, and develop new tools as necessary that can be used, modified and advanced by others
7. Document and communicate (orally and in writing) computational research and results to varied audiences
   and users
8. Acquire, follow, and implement best practices in generating, handling, and analyzing data; develop the ability to critically question the design of algorithms and their purposes; abide by and promote scientific integrity and accountability

Assessment

Students are assessed through the following means:

1. Course Projects in the COMP 670 Seminar: Problems in Computational Science
2. MS Thesis/Final Project Proposal: written and oral presentation
3. Thesis/Final Project Defense
4. Publication of Peer-Reviewed Articles
5. Institutional Data (Time-to-Degree, Graduation Rates)
6. Employment Placement Rates
7. Student Surveys
8. Alumni Surveys