Scientific Computing, High-Performance Computing and Data Science in Higher Education

TL;DR

This paper reviews global academic programs in Scientific Computing, HPC, and Data Science, highlighting a shift towards data-oriented methods and the increasing demand for specialized research computing degrees, with a focus on Canadian initiatives.

Contribution

It provides an overview of current curricula, analyzes enrollment trends, and proposes curricula for new research computing degrees based on empirical data and existing programs.

Findings

Steady increase in research computing instruction demand

Shift from traditional HPC to data-oriented methods

Growing need for specialized research computing degrees

Abstract

We present an overview of current academic curricula for Scientific Computing, High-Performance Computing and Data Science. After a survey of current academic and non-academic programs across the globe, we focus on Canadian programs and specifically on the education program of the SciNet HPC Consortium, using its detailed enrollment and course statistics for the past four to five years. Not only do these data display a steady and rapid increase in the demand for research-computing instruction, they also show a clear shift from traditional (high performance) computing to data-oriented methods. It is argued that this growing demand warrants specialized research computing degrees. The possible curricula of such degrees are described next, taking existing programs as an example, and adding SciNet's experiences of student desires as well as trends in advanced research computing.