Rust, Java, Python, machine learning, Scala, R, C, Visual Basic, C++ and so on. The IT industry is replete with programming languages - and choices. But not all of those choices might make for a long-lasting, well-remunerated and secure career.
And it's not just certificates and experience that organisations are looking for, but attitudes and evidence of a well-rounded individual.
"I choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it," Bill Gates is reported to have said.
Of course, spending many hours on a couch in front of a TV would not be enough to turn Bill's head. Paradoxically his ideal candidate would be possessed of endless energy, with a burning desire to find shortcuts and workarounds combined with the skill, stamina and diligence to see the quest for a life of ease through to the end.
Gates's point was that the best engineers are those whose driving urge is to simplify through technology. This is the goal of machine learning for example. Why look for patterns in data yourself when you can train a machine to do it, and do it much better?
"It's a lot about being intelligently lazy - automating everything a computer can do for you," said Katrina McIvor, principal technologist for DevOps at QA, adding that machine learning and AI are some of the biggest technology growth areas at present.
"They are getting a lot of traction under the heading of data science. It all used to be called data mining, so it's not new, the tools are just better these days and the people in companies are more willing to talk about it."
The goal is to make data science simple not necessarily so that anyone could do it using a dashboard, but so new layers can be built on top of a stable platform.
"The Microsoft Machine Learning stuff will let you train a model and predict outcomes via a web service without you needing to write a line of code," said McIvor. "Of course, knowing how to wire things up together is another thing."
Those looking for a job in tech are generally in the business of wiring things up, and there are a few key languages in the data science area, such as Scala, Python and R.
Scala was also mentioned by Ritika Trikha, a researcher at HackerRank, a platform that allows coders to practise their craft and employers to recruit the most promising developers.
"Among the newer languages we're seeing are Swift, Go and Scala," she said. "There's a lot of interest in those right now."
Apache Spark is written in Scala, which makes it popular with data scientists. It is based on the Java Virtual Machine [JVM] and is very compact once you've learned the ropes.
"The bit I particularly like is that a single line of code is an entire class, complete with constructor, getter, setter, toString, hashcodes the works," said McIvor.
"Writing all that [Java] boilerplate was boring, so why not let the compiler do it for you? It's being intelligently lazy again."
That said, learning Scala might be a bit of a leap for many. With its simple syntax, Python may be a better starting off point. Despite being a relatively old language it is still very relevant today.
"Python is amazingly versatile, it's used for coding, infrastructure stuff, general helper scripts and a lot in the AI / data science area as well these days," explained McIvor.
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