CMDA-3634: Computer Science Foundations for Computational Modeling & 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: 24
Average GPA: 3.10 (rounds to B)
Strict A Rate (No A-) : 38.58%
Average Withdrawal Rate: 2.66%
| Jason R Wilson | 2024 | 52.5% | 22.8% | 14.7% | 5.2% | 3.6% | 1.3% | 3.16 | 9 |
| Eileen R Martin | 2020 | 43.7% | 33.4% | 13.3% | 3.7% | 6.0% | 0.0% | 3.06 | 4 |
| Russell J Hewett | 2021 | 46.7% | 23.2% | 12.2% | 5.1% | 7.2% | 5.7% | 3.00 | 6 |
| Timothy Warburton | 2023 | 56.5% | 17.5% | 13.1% | 3.6% | 5.0% | 4.4% | 3.23 | 3 |
| Johann Rudi | 2023 | 48.1% | 27.1% | 11.2% | 4.4% | 6.8% | 2.3% | 3.05 | 2 |