For more than a decade, Python has been popular among academics, especially in areas such as natural language processing (NLP). As a result, if you have a project that requires NLP processing, you're faced with a dizzying number of choices, including the classic NTLK, topic modeling using GenSim, or the super-fast and accurate spaCy. Similarly, when it comes to neural networks, Python is equally at home, with Theano and Tensorflow; and then there's the machine learning-oriented scikit-learn, and NumPy and Pandas for data analytics.

There's also Juypter/iPython - a web-based laptop server framework that lets you use a ****-enjoyable logging format that mixes code, graphics, and almost any object into the mix. This has always been one of Python's killer features, but these days the concept is proving so useful that it's showing up on pretty much every language that espouses the read-read-output-loop (REPL) concept, including Scala and R.

Python is often supported in big data processing frameworks, but at the same time, it's often not a first-class citizen. For example, new features in Spark are almost always at the top of the Scala/Java bundle, and it may be necessary to write several minor versions of PySpark for those updates (this is especially true for the Spark Streaming/MLLib side of the development tool).

In contrast to R, Python is a traditional object-oriented language, so most developers will be quite comfortable with it, whereas a first encounter with R or Scala can be intimidating. One small problem is that you need to leave the correct white space in your code. This divides people into two camps, those who feel that "it's great for readability" and those who feel that we shouldn't have to force the interpreter to make a program work just because a line of code has a character in the wrong place.

2. The R language

Over the past few years, R has become the darling of data science -- which is now known not just to nerdy statisticians but also to Wall Street traders, biologists, and Silicon Valley developers. Used by companies in a variety of industries, such as Google, Facebook, Bank of America, and the New York Times, R is continuing to spread and proliferate for business use.

The R language has a simple and obvious appeal. With R, in just a few lines of code, you can sift through complex data sets, manipulate data with advanced modeling functions, and create flat graphs to represent numbers. It has been compared to an extremely active version of Excel.

The greatest capitalization of the R language is the vibrant ecosystem that has been developed around it: the R community is always adding new packages and features to its already quite rich feature set. It is estimated that over 2 million people use R, and a recent poll showed that R is by far the most popular language for scientific data, used by 61% of respondents (followed by Python at 39%).

3. JAVA

Java, and Java-based frameworks, were found to have just about become the skeletal scaffolding of those biggest tech companies in Silicon Valley. "If you go to Twitter, LinkedIn and Facebook, then you'll find that Java is the language that underpins all of their data engineering infrastructure," Driscoll said.

Java doesn't provide the same quality of visualization as R and Python, and it's not the best choice for statistical modeling. However, if you move past prototyping and need to build large systems, then Java is often your best bet.