Teaching of Engineering Mathematics in the Era of Data Science and Artificial Intelligence

In the past five decades, the teaching of Engineering Mathematics has continually evolved. In the 1970s and 1980s, when the undergraduate degree was five years in duration, mathematics occupied a prime position in engineering curriculum. When undergraduate programs were shortened to four years, the axe fell on mathematics courses. With the growth of jobs in the Information Technology sector and wide availability of tools for automating engineering tasks, some managers question the very need for teaching engineering mathematics courses, stating that engineering jobs do not require this knowledge since powerful software tools and automated flows are used in practice. Presently, most universities include 4 courses on engineering mathematics. There are universities who have customized the math courses for different departments. Some universities have integrated elements of engineering mathematics into departmental core courses with the intention of teaching the subject in a contextual setting. The availability of mathematical software such as MATLAB, Octave and SCILAB has added a new dimension to engineering education; it has now become possible to make mathematics courses more interesting by including computational experiments. Artificial Intelligence (AI), Machine Learning (ML) and Data Science (DS) have the potential to take engineering math education to a new level. In this paper, our goal is to explore how the teaching of engineering mathematics is evolving in the era of AI, ML, and DS. We consider both the merits and demerits of using AI, ML and DS tools in the math curriculum and provide some recommendations.