CMDA-3634: Computer Science Foundations for Computational Modeling and Data Analytics
Description: Survey of computer science concepts and tools that enable computational science and data analytics. Data structure design and implementation. Analysis of data structure and algorithm performance. Introduction to high-performance computer architectures and parallel computation. Basic operating systems concepts that influence the performance of large-scale computational modeling and data analytics. Software development and software tools for computational modeling. Not for CS major credit.
Pathways: N/A
Course Hours: 3 credits
Sections Taught: 26
Average GPA: 3.08 (B+)
Strict A Rate (No A-) : 40.70%
Average Withdrawal Rate: 3.38%
| Eileen R Martin | 2020 | 43.7% | 33.4% | 13.3% | 3.7% | 6.0% | 0.0% | 3.06 | 4 |
| Timothy Warburton | 2023 | 55.8% | 17.8% | 11.6% | 3.9% | 6.8% | 4.2% | 3.17 | 7 |
| Jason R Wilson | 2024 | 51.8% | 20.9% | 15.1% | 6.2% | 4.9% | 1.2% | 3.09 | 6 |
| Russell J Hewett | 2021 | 46.7% | 23.2% | 12.2% | 5.1% | 7.2% | 5.7% | 3.00 | 6 |
| Johann Rudi | 2023 | 48.1% | 27.1% | 11.2% | 4.4% | 6.8% | 2.3% | 3.05 | 2 |
| Matthew Chalmers | 2018 | 50.1% | 13.3% | 5.9% | 5.9% | 11.8% | 13.2% | 2.93 | 1 |