Role of Algebra and Calculus

Data science is important but Math is a basis for much of data science. We have seen this also in this course. The gradient decent method for example is an important underlying principle for machine learning. Data structures like tensors, seen in the last part of the course are now seen in the name of frame works like tensor flow. Brian Conrad makes an important statement about data science:

"I wish to emphasize a fact of reality: data science undergraduate degrees require calculus, and data science jobs at Spotify, Salesforce, Amazon, Dropbox, Google, LinkedIn, etc. require degrees in fields such as math, economics, engineering, computer science, etc., and these require calculus and linear algebra. There are ``data analytics'' jobs requiring little math, but when all the work is running tools on spreadsheets with no guiding mathematical understanding then the job is on track to being automated. "

These issues have come up in the 2022 revision of the mathematics framework in California. And from this document:

It is misleading, however, to promote data literacy and high-school level data science courses as a substitute for learning math content in preparation for college-level quantitative courses. Algebra, statistics, geometry, trigonometry, and calculus as topics in the high school math curriculum are not interchangeable. The surge of interest in data science as a goal of college degrees and careers is reminiscent of the explosion of interest in computer science that began around 2010 and hasn't let up since. In both cases, math is a foundational tool for such work in both academia and industry. The skills and knowledge acquired in high school math have a big impact on preparedness for these areas in college and beyond. Topics in Algebra II such as logarithms, exponentials, and trigonometry are not relics of the Sputnik era or mere luxuries for future Math and engineering majors. They are foundational across work in quantitative fields including data science, neuroscience, machine learning, statistics, computational biology, and computer graphics. Algebra II in high school is an essential prerequisite to later learn the further math, such as calculus, needed in much of the coursework for undergraduate data science and statistics majors at campuses of the University of California (UC), the California State University (CSU) system, and private institutions such as Caltech and Stanford University. The devaluing of calculus in the promotion of data science (such as here and here) misrepresents its central role in machine learning and data science at large. It also goes against the American Statistical Association's recommendation that all undergraduate statistics degrees require calculus and linear algebra.

There is some discussion in this blog of Peter Wojt. Math Education is a complex issue involving not only the mathematics. It has become also political and philosophical. The question is how to best prepare students about a rather uncertain future. Yuval Harari has put it in this google talk as follows:

It's really the first time in history that we don't really know what particular skills to teach young people, because we just don't know in what kind of world they will be living.

As a math teacher one might be biased but there is no doubt that algebra and calculus will without doubt be important in the future, whatever it brings. Math is of course not the only part. A good education also requires knowledge in many other areas like art, history or literature as well as other sciences.