High-Performance Computing Program Overview
Graduates of the Master of Science in Data Science and Analytics who pursue the High Performance Computing (HPC) emphasis area will achieve the following educational objectives, in addition to the core program objectives while becoming immersed in Big Data computational ecosystems.
Graduates of the Master of Science in Data Science and Analytics who pursue the High-Performance Computing (HPC) Emphasis Area will achieve the following educational objectives, in addition to the core program objectives while becoming immersed in HPC concepts:
- Students will have an in-depth understanding of state-of-the-art technologies that enable big data analytics and high-performance computing; such that they can successfully investigate the data and analytical needs, then guide the decision-making process on deployments into HPC infrastructure.
- Students will acquire knowledge to exploit cloud-based computing infrastructure, including virtualization, distributed architectures, on-demand resource scaling, container technology, and other cloud-based computing concepts in support of Big Data management, processing, and analytics.
- Students will have a thorough understanding of advanced technologies and techniques in Big Data analytics which facilitate the extraction of new data intelligence using state-of-the-art, leading analytical platforms.
- Students will gain a solid understanding of techniques for exploiting advanced co-processing hardware, including graphics processing units (GPU) and many-core units (e.g., Intel Phi) to achieve cost-effective, massively parallel data analytics.
High-Performance Computing Faculty
Sample Course Path for MS Online
Students move through 8-week modules completing core courses and then progressing through emphasis area courses directly applicable to their area of study.