CMDA-4634: Scalable Computing for Computational Modeling and Data Analytics
Description: A focused study of concepts and tools that accelerate computational and data science at scale. Design, analysis, optimization, and modeling of application-driven algorithms suitable for state-of-the-art scalable computing platforms. Software development and engineering for scalable computational science.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 3
Average GPA: 3.56 (rounds to A-)
Strict A Rate (No A-) : 60.76%
Average Withdrawal Rate: 7.14%
| Timothy Warburton | 2024 | 72.9% | 8.4% | 7.8% | 3.7% | 0.0% | 7.1% | 3.56 | 3 |