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.

Courses

Data Analytics from Applied Machine Learning

Synopsis:  This course leverages the foundations in statistics and modeling to teach applied concepts in machine learning. Participants will learn various classes of machine learning and modeling techniques, and gain an in-depth understanding how to select appropriate techniques for various data science tasks. Topics cover a spectrum from simple Bayesian modeling to more advanced algorithms such as support vector machines, decision trees/forests, and neural networks. Students learn to incorporate machine learning workflows into data-intensive analytical processes.
3 – Credit Hour

Cloud Computing for Data Analytics

Data Analytics from Applied Machine Learning

Synopsis: This course leverages the foundations in statistics and modeling to teach applied concepts in machine learning. Participants will learn various classes of machine learning and modeling techniques, and gain an in-depth understanding how to select appropriate techniques for various data science tasks. Topics cover a spectrum from simple Bayesian modeling to more advanced algorithms such as support vector machines, decision trees/forests, and neural networks. Students learn to incorporate machine learning workflows into data-intensive analytical processes.

3 – Credit Hour

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 and various other parallel computing architectures.  Students will research emerging parallel and scalable architectures for data analytics.

3 – Credit Hour

Outcomes

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.

High-Performance Computing Faculty

Shaji Khan

Associate Professor

234 Express Scripts Hall-St Louis campus
(314) 516-6279
shajikhan@umsl.edu

Grant Scott

Assistant Research Professor; Co-Director for Industry Outreach, Course Coordinator

W3038 Lafferre Hall
(573) 884-6400
grantscott@missouri.edu

Yi Shang

High Performance Computing Course Coordinator

207 Naka Hall
(573) 884-7794
shangy@missouri.edu

Jeff Uhlmann

Associate Professor

217 Naka Hall
(573)884-2129
uhlmannj@missouri.edu

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.

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