A long, long time ago in what seems like a galaxy far, far away, the only coding that ever happened in any workplace was by people who had "programmer" or "developer" in their job titles. One exception to this was in Universities where coding did play an important part in some narrow fields of academia.
Fast forward to 2020 and this is no longer the case. Programming has very clearly broken out of its narrow niche from a decade or two ago. There are still of course many full-time programming jobs in our economy as the demand for software and technology keeps increasing. But this blog post is not about those jobs.
Coding is of particular importance today in any field that has lots of data to deal with. And data can take many forms. We often think of data as a set of numbers, and in many fields that it true. Finance was an early adopter of using coding because of the need to understand long series of numbers.
But data can be more diverse than that. Transport for London for example have data about how many people touch in and out of every station at all times of the day, and the journeys that people make. They also have data about bike rides that are done and bus journeys. In order to understand how people move around London better and how to adapt the transport services accordingly, these data need to be analysed together. There is too much information for people to be able to do this manually. That is why we need to write a computer program to get answers for the questions people want to ask from the data.
And all industries are collecting more and more data than they ever did in the past. In some cases there is scope to hire one or more people dedicated to getting answers from these data, but more and more often, people with other responsibilities other than coding and data science need to be able to write programs themselves.
This is true both in larger companies and smaller ones. We often speak to employees of large corporations with teams of data scientists and programmers who tell us they want to learn programming because there are times when they have a question that needs an answer promptly. The delays in submitting a request to the data science team and getting an answer back are not acceptable. It is much easier and more efficient if the person who needs the result writes a program to get those results themselves.
In smaller organisations, the problem is a different one. There are usually no data scientists and therefore the options are either to outsource or simply to give up trying to make the most of the data available. The latter tends to be the chosen route, putting smaller organisations even more at a disadvantage compared to larger ones.
We finish off back in the academic world. Coding has long been a key tool in certain areas of academia, such as Physics and Maths (and Computing, of course). But this has now moved well beyond these subjects. We now have entire fields of computational chemistry and biology that rely on coding to explore their scientific questions; data collected in social science and economics research is being crunched more than ever before, again because of computer programs written to do this job; thousands of pages of case law can be analysed to look for specific areas of interest in some legal domain. There are very few fields of study left that don't benefit from programming.
All of the above discusses how programming is used in more and more jobs today. The languages used vary a lot and there is no single language that is "better" than the others. The reason we still have many programming languages around is because there isn't one language to rule them all. The choice of language for a certain task depends on two things broadly: some languages are better suited for some tasks, and the preference of the person writing the program.
Python is not the solution to every problem. I am a big fan of Python and I (personally) use it for every need I currently have. However I'm not a fan of the "language wars" that go on in many places. When we teach coding, to all ages, we stress that what matters is learning coding, not a coding language.
So why is Python so popular in many fields? Python is a language that lends itself to writing code quickly, unlike some other more rigorous languages. This means that if someone has a question that needs answering using a certain data set, it won't take long to write the code to get that answer. Python is also a general purposes language and not restricted to just a few applications. This can be seen from the vast number of modules available in Python covering anything from astronomy to AI and finance, and pretty much anything else. Not many languages allow 7 year olds to code animations and astrophysicists to discover the secrets of our Universe.
We cannot predict what the skills needed for jobs in 50 years' time will be. But what is clear is that the current trend is for programming to be a part of the skill set for many jobs, and that trend is unlikely to stop any time soon.
Join us to start learning how to code in Python. The first hurdle is often the toughest but we have specialised in helping beginners overcome that and to bring them quickly to a point where they feel confident to start to tackle the questions they need to ask in their jobs.