programming languages worth learning no

9 cutting-edge programming languages worth learning now

These strong alternatives to the popular languages are gaining steam -- and may be the perfect fit for your next project

The big languages are popular for a reason: They offer a huge foundation of open source code, libraries, and frameworks that make finishing the job easier. This is the result of years of momentum in which they are chosen time and again for new projects, and expertise in their nuances grow worthwhile and plentiful.

Sometimes the vast resources of the popular, mainstream programming languages aren’t enough to solve your particular problem. 

Sometimes you have to look beyond the obvious to find the right language, where the right structure makes the difference while offering that extra feature to help your code run significantly faster without endless tweaking and optimizing. 

This language produces vastly more stable and accurate code because it prevents you from programming sloppy or wrong code.

The following nine languages should be on every programmer’s radar. They may not be the best for every job -- many are aimed at specialized tasks.

But they all offer upsides that are worth investigating and investing in. There may be a day when one of these languages proves to be exactly what your project -- or boss -- needs.

Erlang: Functional programming for real-time systems

Erlang began deep inside the spooky realms of telephone switches at Ericsson, the Swedish telco. When Ericsson programmers began bragging about its "nine 9s" performance, by delivering 99.9999999 percent of the data with Erlang, the developers outside Ericsson started taking notice.

Go: Simple and dynamic

Google wasn’t the first organization to survey the collection of languages, only to find them cluttered, complex, and often slow. In 2009,
the company released its solution: a statically typed language that looks like C but includes background intelligence to save programmers from having to specify types and juggle malloc calls. With Go,
 programmers can have the terseness and structure of compiled C, along with the ease of using a dynamic script language.

While Sun and Apple followed a similar path in creating Java and Swift, respectively, Google made one significantly different decision with Go: The language’s creators wanted to keep Go "simple enough to hold in one programmer's head."

 Rob Pike, one of Go’s creators, famously told Ars Technica that "sometimes you can get more in the long run by taking things away." Thus, there are few zippy extras like generics, type inheritance, or assertions, only clean, simple blocks of if-then-else code manipulating strings, arrays, and hash tables.

Groovy: Scripting goodness for Java

The Java world is surprisingly flexible. Say what you will about its belts-and-suspenders approach, like specifying the type for every variable, ending every line with a semicolon, and writing access methods for classes that simply return the value. But it looked at the dynamic languages gaining traction and built its own version that's tightly integrated with Java.

Groovy offers programmers the ability to toss aside all the humdrum conventions of brackets and semicolons, to write simpler programs that can leverage all that existing Java code. Everything runs on the JVM.

OCaml: Complex data hierarchy juggler

Some programmers don't want to specify the types of their variables, and for them we've built the dynamic languages. Others enjoy the certainty of specifying whether a variable holds an integer, string, or maybe an object. For them, many of the compiled languages offer all the support they want

Julia: Bringing speed to Python land

The world of scientific programming is filled with Python lovers who enjoy the simple syntax and the freedom to avoid thinking of gnarly details like pointers and bytes.

 For all its strengths, however, Python is often maddeningly slow, which can be a problem if you're crunching large data sets as is common in the world of scientific computing.

To speed up matters, many scientists turn to writing the most important routines at the core in C, which is much faster. But that saddles them with software written in two languages and is thus much harder to revise, fix, or extend.

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