Why should I learn programming?

During every aircraft development, in order to assure that the flying machine behaves exactly as it was designed for, the flight test is performed by the manufacturer, also to prove to the competent authorities that the aircraft is able to fly safely. For the flight test purposes, the aircraft is equipped with all sorted of sensors (let’s say around 100,000 parameters), whose measured data we could assume recorded each 10 milisseconds. Doing a rough calculation we conclude that a single hour of flight test produces around 270 Gigabytes of data (in real life is actually much more than that).

The main point here is that for a single flight, there is a huge amount of data available to the aeronautical engineers validate and improve their design. But this only can happen in a phase of data analysis, which comes after few steps of processing the raw data, obviously unthinkable without some programming work. In a worst case scenario, the aeronautical engineers with their skills provided by a semester-long course of structured programming will struggle to write a little script from scratch to process the data and to allow them to check (for the thousandth time) if the aircraft behaved as expected.

This is not an isolated case of aeronautical engineering. Nowadays, in any field of science or engineering, our capacity to generate data has increased dramatically. For instance, the European Organization of Nuclear Research (CERN) announced (already in 2013) that their data centre had 100 petabytes of stored data, “equivalent 700 years of full HD-quality movies”. And the amount of general data produced daily is even more impressive. In every minute YouTube users share 400 hours of new videos, Facebook users share 216,302 photos, Instagram users like 2,430,555 posts.

This tells us that a problem-solving in this world overwhelmed by data is more and more walking towards formulate the problem for the computer helping us to solve it, no matter the field of knowledge we are talking about. This requires knowing how to ask the machine to solve our problem. As stated by an important Brazilian researcher, “every science is a computer science”.

Computer Science gap in other Sciences/Engineerings

If we compare a programming course in a standard engineering graduation today and 30 years ago, we would be impressed how topics are pretty much the same, sometimes only replacing Fortran by C, Python or even Matlab. Of course the basics of algorithms and problem-solving is very important to be kept, but the new engineers should at least be aware of the newest capabilities of programming and even the tools and techniques supporting the programming activity.

Resuming our example in the beginning of this text, aeronautical engineers 20 years ago could handle well (or, let’s say, using cutting edge technology by that time) the data available with their piece of Fortran code. Today, after getting (with a couple of clicks) the hundreds of gigabytes of data to analyze, they write a piece of code not that different than the one 20 years ago (in a different language, hopefully), but that runs in a hardware brutally faster and eventually achieve the expected results. Certainly, their job could be done faster by improving the code or by some different problem-solving technique; or perhaps many small repetitive tasks in the middle could be automated.

We could summarize this scenario as follows: in one hand we have engineers that eventually can make their job done, but is not fully aware that it could be improved by the latest programming capabilities available; on the other hand, we have computer engineers able to apply the best techniques but not required to help the other (non-computer) engineers.

Here we plotted the issue in the engineering field that is certainly far from being the worst case scenario, once that, at least, they have a programming course as part of their graduation. If we think about Human and Health sciences, the computer science gap may be much bigger.

Coding at School

Some people believe that in order to get over the aforementioned gap, we should go further and teach programming and related computer science topics earlier, at basic school. Hadi Partovi, the CEO of Code.org (a non-profit organization that aims expanding the access to computer science) said that “we don’t teach biology or chemistry to kids because they’re going to become surgeons or chemists, we teach them about photosynthesis and that water is H2O, or how lightbulbs work, just to understand the world around us. You don’t use any of it, but you do on a day-to-day basis use public-key encryption, and the average American has absolutely no idea what that is” (full text).

Besides initiatives as Code.org, programming contests worldwide has been becoming popular among youngsters before grad schools. The Brazilian Computer Society, for example, since 2002, offers modalities even for students under high school. The students shall solve computing and logical reasoning problems using just pencil and paper and, according to the Society, the goal is “to awaken the interest in computing problems and discover potential talents to programming”. The students with the best results in Brazilian Olympiad in Informatics will represent the country in the International Olympiad in Informatics which started at 1989 with 46 contestants from 13 countries and in its latest edition (2016) had 308 contestants from 80 countries.

Many countries have already included programming or “computational thinking” in their elementary school curricula. At European Union, 15 countries made it. And, at least in Brazil, where programming is not yet at the young students curriculum, private schools for “programming and robotics for teens” are rising in great cities like São Paulo, picturing a similar phenomenon which happened many years ago with the english private schools created all over the country. Sounds like the world is requiring the kids to learn a new language.

What about the future?

Studies published at the World Economic Forum Annual Meeting last year (2016) pointed some interesting facts about the jobs of the future: "The study shows that workers who successfully combine mathematical and interpersonal skills in the knowledge-based economies of the future should find many rewarding and lucrative opportunities."

If you realise that you can describe the routine of your day-to-day job in repetitive and well defined steps to execute some task, trust me, a computer program can do it way better than you can. This may sound alarming, but I can assure you that a significant percentage of a standard day at work of even very specialized science/engineering fields are covered by very repetitive tasks.

If at this point you are not convinced that you should improve your computational thinking skills, I quote the disturbing statement of this Brazilian researcher: “at this very moment, someone in the world is programming your job”.