For data science and big data, R, Python and Scala are the 3 most important languages to master. It’s a widely-known notion, organizations of varying sizes relies on massive structured and unstructured data to predict trends, patterns and correlations. They are of expectation that such a robust analysis will lead to better business decisions and individual behavior predictions.
In 2017, the adoption of Big Data analytics has spiked up to 53% in companies – says Forbes.
The story of evolution
To start with, big data is just data, after all. The entire game-play depends on its analysis – how well the data is analyzed so as to churn out valuable business intelligence. With years, data burgeoned, and it’s still expanding. The evolution of big data mostly happened because traditional database structures couldn’t cope with such multiplying data – scaling data became an important issue.
For that, here we have some popular big data programming languages. Dive down:
R Programming is mainly used for statistical analysis. A set of packages are available for R named Programming with Big Data in R (pbdR), which encourages big data analysis, across multiple systems via R code.
R is robust and flexible; it can be run on almost every OS. To top that, it boasts of excellent graphical capabilities, which comes handy when trying to visualize models, patterns and associations within big data structures.
According to industry standards, the average pay of R Programmers is $115,531 per year.
Compared to R, Python is more of a general-purpose programming language. Developers adore it, because it’s easy to learn, a huge number of tutorials are available online and is perfect for data analysis, which requires integration with web applications.
Python gives excellent performance and high scalability for a series of complicated data science tasks. It is used with high-in-function big data engines, like Apache Spark through available Python APIs.
Their Machine Learning Using Python courses are of highest quality and extremely student-friendly.
Last but not the least, Scala is a general-purpose programming language developed mainly to address some of the challenges of Java language. It is used to write Apache Spark cluster computing solution. Hence, Scala has been a popular programming language in the field of data science and big data analysis, in particular.
There was a time when Scala was mandatory to work on Spark, but with the proliferation of many API endpoints approachable with other languages, this problem has been addressed. Nevertheless, it’s still the most significant and popular language for several big data tools, including Finagle. Also Scala houses amazing concurrency support, which parallelizes a whole many processes for huge data sets.
The average annual salary for a data scientist with Scala skills is $102,980.
In the end, you can never go wrong with selecting any one of the big data programming languages. All of them are equally good, productive and easy to excel on. However, Python is probably the best one to start off with.
The original article is here at – http://www.i-programmer.info/news/197-data-mining/11622-top-3-languages-for-big-data-programming.html
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