High-Performance Computing
Emphasis Area

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.


Parallel Computing for Data Science
Synopsis: This course will provide in-depth treatment of the evolution of high performance, parallel computing architectures and how these architectures and computational ecosystems support data science. We will cover topics such as: parallel algorithms for numerical processing, parallel data search, and other parallel computing algorithms which facilitate advanced analytics. To reinforce lecture topics, learning activities will be completed using parallel computing techniques for modern multicore and multi-node systems. Parallel algorithms will be investigated, selected, and then developed for various scientific data analytics problems. Programming projects will be completed using Python and R, leveraging various parallel and distributed computing infrastructure such as AWS Elastic Map Reduce and Google Big Query. Students will research emerging parallel and scalable architectures for data analytics.
3 – Credit Hour
Data Science with Cloud Computing
Synopsis:This course will explore the principles that integrate computing theories and information technologies with the design, programming and application of distributed systems. The course topics will familiarize students with distributed system models and enabling technologies; virtual machines and virtualization of clusters, networks and data centers; cloud platform architecture with security over virtualized data centers; service-oriented architectures for distributed computing; and cloud programming and software environments. Additionally, students will learn how to conduct some parallel and distributed programming and performance evaluation experiments on applications within available cloud platforms. Finally we will survey research literature and latest technology trends that are shaping the future of high performance, distributed and cloud computing.
3 – Credit Hour
Big Data Algorithms & Ecosystems
Synopsis: Big Data represents a new era of computing, where data in any format maybe processed and exploited to extract insights for industries and organizations to make informed decisions, whether that data is in-place, in-motion or at-rest, in large volume, structured or unstructured. This course will cover advanced computational technologies, Big Data ecosystems, and techniques that enable industries to extract insights from data with sophistication, speed and accuracy. You will learn practical industry best practices to bridge the gap between classroom learning and real world.
3 – Credit Hour


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 the state-of-the-art technologies which 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.


Shaji Khan

Associate Professor

234 Express Scripts Hall-St Louis campus
(314) 516-6279

Yi Shang


207 Naka Hall

Jeff Uhlmann

Associate Professor

217 Naka Hall

Sample Course Path

Students move through 8 week modules completeting core courses and then progressing through emphasis area concentration courses directly applicable with their area of study.


More Information

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