The era of the mathematician has arrived

When I studied (chemical) engineering, the second most important topic to master was mathematics. Without that, you could not comprehend things like quantum mechanics. I wasn't the most brilliant mind when it comes to math(s), but I definitely could feel the music. We took our classes in the maths faculty of my university, and so I came to meet some maths students that later on became my friends. At the end of their interesting study, my friends - the fresh mathematicians feared that there was no use for deep algebra in their future career. Because where do you go when your only practical experience is spending days writing Greek symbols on whiteboards?

Today, there is no better field to start out in than mathematics if you want to make sense of everything new that is happening around us. And don’t just take this from me — the job market is realizing this too. In a recent report, the US Bureau of Labor Statistics projects that the job market for Mathematicians and Statisticians will grow by a whopping 33% in the next decade, and calculates that Mathematicians already earn almost three times the US average salary.

Only those with a math background can truly make sense of the tsunami of analytics that is already on our shores

Why is mathematics suddenly so important?

I would argue that it has always been important in most fields to have mathematical training. Highly trained mathematicians learn a discipline to their thought processes that inject calmness and assuredness to the people they work with. In the highly mathmetized (is that a correct word?) business environments, where quantitative and qualitative facts are coming at us from all angles, there is a great need for systematic thinkers who have a logical approach and can quickly reduce problems to their crux.

But despite this, I never imagined even five years ago that the actual content of what I learned back then would come back to be such a massive part of what I do now. How would multidimensional linear algebra, discrete mathematics, matrix theory, combinatorics, logic, and probability be of any use to me in a career in the world of business?

Subscribe to the TechTonic Shifts newsletter.This newsletter cover a broad range of tech topics like Digital Transformation, Machine Learning, Cybersecurity, Data Engineering/Governance, IoT, to even highly exotic future tech.

The explosion of big data, data science and AI changed all that. Now, amazingly, I re-engage with all these topics on a day-to-day basis. Here are some examples of what I mean:

• A linear algebra background helps your deeply understand how feature spaces in Machine Learning operate, how learning algorithms like Support Vector Machines work, as well as many of the modern embedding techniques in natural language processing. This means you can interpret results more effectively but also explain these methods to others in a relatable way.
• Training in discrete mathematics helps you with topics like graphs and networks as well as cryptography, all of which I have to think about or work with regularly.
• Underlying classes of matrices and matrix properties like eigenvectors and eigenvalues are critical foundations to many computational techniques today, including algorithms, network analysis, and statistical models.
• Combinatorics is an incredible training ground for programming in general, and functional programming in particular, all of which are much-needed skills in the workplace today.
• Logic and probability are foundational for statistical hypothesis testing, something I regard as the biggest gap in decision-making skills in today’s data-rich world.

The era of the mathematician is here

The era of the mathematician has arrived. No longer will academic pursuits be the only realistic option for those who want to immerse themselves in this rather beautiful world that I used to love alongside engineering, and to which more recently I have returned. We are already starting to see, and will continue to see, an impressive growth of enterprise roles as mathematics practitioners, many of which are directly impacting outcomes in the world around us.

If you are a young person reading this, and still considering what you want to train in or study, you probably know what I would tell you. If math is your passion, stick with it. It’s going to be the skill of the future.

But even if you are a bit further on in your career but have always been mathematically inclined like me, it’s never too late to become more involved again in maths. The resources are plenty and the options are varied. I’d highly recommend it if you have the time and interest.

Subscribe to the TechTonic Shifts newsletter

Well, that's it for now. If you like my article, subscribe to my newsletter or connect with me. LinkedIn appreciates your likes by making my articles available to more readers.

Signing off - Marco

Other articles you might be interested in:

• Human-Centered AI design and the HAX toolkit | LinkedIn
• The next big thing is Tiny (ML)! | LinkedIn
• A model for strategy development in the AI age | LinkedIn
• How AI is preventing cyber attacks [including e-book] | LinkedIn
• The Machine Learning Lifecycle and MLOps | LinkedIn
• 150 Digital Transformation templates new ones added | LinkedIn
• Knowledge graphs for Machine Learning are so cool ! | LinkedIn
• The AI Readiness Index - a brilliant framework for assessing AI adoption in your organization | LinkedIn
• What is explainable AI (XAI) | LinkedIn