This online research computing specialization introduces learners to the fundamentals of high performance and parallel computing and includes big data analysis, machine learning, parallel programming, optimization, and more.

By completing this specialization, you will be able to:

  • Acquire, clean, wrangle, and manage data
  • Correctly perform exploratory data analyses in order to assist with the generation of scientific hypotheses.
  • Describe the components of a high-performance distributed computing system
  • Navigate a typical Linux-based HPC environment
  • Assess and analyze application scalability including weak and strong scaling
  • Quantify the processing, data, and cost requirements for a computational project or workflow
  • And much more


  • Introduction to High-Performance and Parallel Computing
  • Efficient Programming (forthcoming)
  • Parallel Programming with MPI (forthcoming)

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program.

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