Math is called the Queen of Sciences. Very powerful, like the Queen in chess. Perhaps that is why, many fear her. Many students study math to pass exams, and when they enter the industry, take leave of Math – both the books and from the mind – with glee.

For several decades, jobs in the industry meant “to be practical, to make things really work, not theoretical and mathematical stuff on paper”. This view is still wide-spread. People were, by and large, quite successful without math. If some math-literate guy played with equations and numbers in the industry, he was branded “too academic to fit in the industry”. It is an unwritten norm in the industry not to have math on the PPT slides, since even the audience can’t understand math well.

*Not any more. The Queen is back.*

The chasm between academy and industry, between theory and practice, is closing.

Today’s world is full of competition – be it in products or services. And there are too many players in every game. In most cases, it is a “close shave” in the races. Most of the times, competitors offer just a subtle differentiation; one which goes un-noticed by the untrained eye. With so many choices in every show-room, it is actually tough for the end-user to choose between competitive products/services. Each vendor points out the very fine difference between his offering and the competition, but the end-user is not sure whether that difference is significant.

The competition may be in full products and services, or in specialized components. Often, the differentiation at a product-level translates to differentiation in the enabling components. It is very challenging for the component-developers to develop “world-best” output.

*Best in the world. First in the world. *These are such catchy phrases which entice the general public. But, behind the scenes, it is a tough life for those techies who make it happen.

To make something *best in the world, *or *first in the world, *one needs to make measurable stuff. Otherwise, there is no way to claim and prove these words. (Yet, many salesmen get away with it, thanks to the gullible public.).

Measurements, measures, numbers, etc. … soon one is drawn into the intricacies of the underlying math. To be successful means to be competitive, to prove that one is best *with specifics and with numbers.*

Math is all-pervasive, for serious players. Can you imagine civil engineering without trigonometry? This is actually how constructions were done even in ancient times – constructions of temples, pyramids, etc. demanded knowledge of math. Navigation in the deep-seas, Astronomy, Economics, Stock market analysis, Manufacturing, Genetics, etc. – you will find math everywhere.

One may ask – *But what about the IT field? Do we need math there too?*

If one is a developer of web services, one has to prove that what he offers to the end-user is within bounds of some latency-limits. So he has to measure the time consumed, memory required, etc. If one is a developer of a Multimedia codec, one has to prove that what he offers is compliant to the standards, measure how much compression he achieves, how fast it runs on which processor, etc. If one is a developer of a communication component, one has to measure the data-rates, the bit-error-rates, the buffer management details, resilience to noise, tolerance to environment changes, how fast it runs on which processor, how much memory it needs, etc. If one is a developer of a medical diagnostics component, one has to measure the accuracy of the diagnosis, the rate of false-errors, the speed of diagnosis, etc.

The list is endless. In each case, after measuring, one needs to compare it with competition, for his component to sell. While measuring is the easier part, to beat the competition is tough. One needs to try many techniques to be able to extract maximum advantages to offer small differentiations.

Measuring, in the first place, is not easy. Identifying meaningful items to measure, determining how to measure, etc. can be a big challenge. Often things are obvious. Physical parameters, temperatures, pressures, etc. are perhaps easier to measure. But there are few cases where one relies on human perception. Quality of images, videos, etc., for instance. Here too, practitioners often employ a measure called Mean Opinion Score, where you ask some people how they perceive the quality, note their responses quantitatively, and come out with a weighted average as the measure. This is only because of a lack of some better measure.

The developer needs to have a firm grip on what he develops. He needs to do a complexity analysis of the under-lying algorithm to ensure that it is within limits affordable by the platform and is scalable.

So, it is obvious that one can’t escape Math. It is imperative that the serious developer learns and maintains a firm grips on some math tools. Geometry, Trigonometry, Calculus, Differential equations, Statistics, Linear algebra, etc. are all critical to the industry. Let us see some examples:

- Civil engineering, Computer graphics: Geometry, Trigonometry
- Pattern recognition, Machine learning, Medical diagnostics: Fourier transforms, Calculus, Linear algebra, Statistics
- All software: Computational complexity
- Databases: Statistics, Linear algebra

In today’s competitive world, not having a firm grip on math can quickly lead one to mediocrity and failure. While I have emphasized on math in this post, it is equally true for all “theoretical domain knowledge”.

It is best to love math and theory, instead of doing it unwillingly. The more you do, the more confident you will be, and the more you will discover the beauty and elegance in her.

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