Get started with MU
Apply hereMaster of science in data science and analytics
The amount of data available continues to grow. Structured and unstructured forms present opportunities for businesses to better engage audiences, for scientific breakthroughs via innovative research and for greater understanding of populations. Yet, this proliferation has one dilemma — many data science and analytics tools aren’t powerful enough to process the amount of information available, leading to a limited picture and missed opportunities. In response, high-performance computing increasingly intersects with the field to provide these capabilities and help draw detailed insights from massive, complex data sets.
The University of Missouri’s (Mizzou) online master of science in data science and analytics program with an emphasis in high-performance computing (HPC) takes a two-part approach based on the industry’s latest developments and applications. You’ll start with an intensive, hands-on core thoroughly diving into the foundation of data science and analytics, including tools, technologies, ethics and capabilities, as well as its languages and mathematical basis. From here, the HPC emphasis focuses on the tools and technologies for mining, storing, analyzing and visualizing large-scale data sets, including networking, architecture, parallel processing, algorithms and other computational techniques.
Explore other emphasis areas from Mizzou's master of science in data science and analytics:
Quick facts
Official name
Master of science data science and analytics with an emphasis in high performance computingCampus
Program type
Master's degreeAcademic home
Graduate School | MU Institute for Data Science and InformaticsDelivery mode
100% onlineAccreditation
Higher Learning CommissionCredit hours
34Estimated cost
$33,422.00*This cost is for illustrative purposes only. Your hours and costs will differ, depending on your transfer hours, your course choices and your academic progress. See more about tuition and financial aid.
Why earn a master’s degree in high-performance computing?
High-performance analytics is a hybrid discipline created through the intersection of data science and analytics and high-performance computing. HPC tools and methods, including cloud technologies, infrastructure, software and parallel supercomputers, drive efforts to examine large, multifaceted data sets. Insights extracted shape decisions for businesses, engineering projects and government agencies and identify trends for scientific and academic research. Considering these possibilities, demand for data science and analytics professionals with high-performance computing knowledge has grown across multiple industries.
Our program was ranked No. 9 by Fortune.com for "Best Online Master's in Data Science Programs in 2023". This underscores Mizzou's mission to meet the high demand for training today's top data science professionals. Mizzou’s online master’s in data science and analytics program welcomes professionals from a broad range of backgrounds interested in exploring these technologies and their applications. Through a core, emphasis area and two hands-on projects, students curious about high-performance analytics:
- Receive a multidisciplinary introduction to data science, including general and HPC-specific tools and techniques for acquiring and assessing large sets, visualizing insights, answering questions and influencing decisions
- Get introduced to statistical analysis, communication techniques, database system design and management, machine learning, data mining, information retrieval and programming languages
- Explore the intersection between big data and high-performance computing, including for structuring and assessing data for decision-making purposes
- Grow their knowledge of cloud-based computing infrastructures and architectures for virtualization, resource scaling and technologies involved in managing, processing and examining data
- Learn how to use the tools, techniques and coprocessing hardware for large-scale parallel data analytics, including graphic processing and manycore units
- Become familiar with business intelligence technologies and platforms for data extraction and analysis
- Gain experience in analyzing real-world data sets while working alongside faculty members and industry professionals
- Begin to understand ethics and security principles for protecting sensitive information
Career prospects
Become an asset to businesses, research teams and government agencies. Your knowledge of high-performance computing and data science helps these organizations tackle the surging amount of information out there, be it identifying patterns, influencing new programs or developing a more informed growth strategy.
Considering the overlap between these two areas, the Bureau of Labor Statistics has identified 22% more positions for computer and information research scientists between 2020 and 2030. Potential job titles include:
- Operations research analyst
- Computer systems analyst
- Market research analyst
- Performance engineer
- Solutions engineer
Program structure
The online master’s in data science and analytics with an emphasis in high-performance computing involves 34 credit hours. This is segmented among core courses (19 credit hours), the emphasis area (nine credit hours), a case study (three credit hours) and a capstone project (three credit hours).
All courses are 100% online, making this program ideal for working professionals interested in expanding their skills or changing careers. During the spring semester, you are invited to attend an optional on-campus Data Science Week, where you can collaborate with and gain real-world insights from our faculty members and partnering industry professionals.
Although the program uses a semester format, courses are held in eight-week modules. Students attending full time finish in roughly two years.
As the building blocks for the high-performance computing emphasis area, the core courses provide a rigorous, immersive introduction to data analytics fundamentals. You’ll learn about its statistical foundation and techniques for accessing, cleansing, modeling, analyzing and visualizing data. Courses further focus on data storage, including the structure and functions of database systems, industry regulations and ethics, data collection principles, security and applied machine learning and modeling.
Mizzou understands that you seek to translate these skills to the workforce. Guiding you in this direction is an eight-week, hands-on group case study followed by a capstone project requiring you to analyze a large, complex data set in conjunction with faculty members and industry experts.
High-performance computing courses
Build off your knowledge of data science and analytics with courses going over:
- Data mining and information retrieval techniques and query languages for identifying hidden and predictive patterns, modeling, indexing and use with unstructured data
- Cluster and cloud computing for data analytics, including platforms, architectures, uses, algorithms and scaling techniques
- Parallel computing in data science, including the incorporation of high-performance parallel computing, architectures, processes and algorithms
Delivery
100% onlineCalendar system
Semester-basedTypical program length
2 yearsAccreditation
The University of Missouri is accredited by the Higher Learning Commission, one of six regional institutional accreditors in the United States.
Faculty spotlight
Dr. Khan is currently developing cybersecurity programs at the University of Missouri-Saint Louis and teaches data networking and information security. Dr. Khan has published in journals, such as the Journal of Business Research, Journal of Strategic Information Systems, Journal of Information Technology and more. He has also presented papers at national academic conferences, authored book chapters and produced practitioner oriented research and industry reports.
His research interests include offshoring of business services, innovation and entrepreneurship and management of information technology and information security in high-reliability environments.