In the first part of the blog on Best Programming Languages for Data Science, we talked about 7 languages. Those included the languages which are being used by maximum of the people dealing with Big Data.
In this blog, I am listing the other half of the list which comprises of the new comers with respect to the programming languages in the first part. Some of them have gained popularity similar to Java, Hadoop, R and SQL whereas, others have made a remarkable place in the market because of the distinguished features offered by them.
List of Programming Languages for Data Science:
1. Python –
Python is one the best open source programming languages for working with the large and complicated data sets needed for Big Data. Python has gained popularity among the programmers using the object oriented languages. Python is intuitive and easier to learn than R, and the platform has grown dramatically in recent years, making it more capable for the statistical analysis like R. Python’s USP is the readability and compactness.
Modern day applications such as Pinterest and Instagram are built using Python. It’s a traditional object-oriented language, one that stresses added levels of productivity and readability. Python will also be a best fit for big data projects dealing with neural networks.
2. MATLAB –
MATLAB is among the best programming languages for data science if you have to work with matrixes. It is not an open source language but is mostly used for academics because of its suitability for mathematical modelling and data acquisition. MATLAB was designed for working with matrixes in the first place which makes it a very good option for using it for statistical modelling and algorithm creation. MATLAB is also good for data science tasks that involve linear algebraic computations, simulations and matrix computations.
The drawback with MATLAB is that it poses restrictions on code portability.
3. Scala –
Scala programming language is a fusion of object oriented and functional programming languages that helps build robust and scalable data science applications. Hence, it works with both Java and Javascript. Scala combines many of the beneficial features of other languages into one tight, easy-to-use tool.
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Scala is based on Java and the compiled code runs on the JVM ecosystem, which makes it potent and flexible out of the gate, as it can run on just any platform. Scala for data science requires a little extra knack of abstraction and thinking. Scalability and number crunching features of Scala have made it among the best programming languages for data science.
4. Hive QL –
Apache Hive is a data warehouse infrastructure built on top Hadoop for providing data summarization, query, and analysis. Hive QL is the Hive query language which has SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Hive doesn’t offer support for row-level inserts, updates, and deletes.
Hive QL is designed to work on top of Apache Hadoop or other distributed storage platforms such as Amazon’s S3 file system. The Hive concept of a database is essentially just a catalog or namespace of tables. With Hive we get the necessary abstraction of SQL to implement Hive QL queries onto the Java API without implementing the queries in the low-level Java API.
5. Julia –
Julia is comparatively new among the Data Languages. Well, the most opted languages are R, Python and Java. But there are still gaps to be looked for. Julia being know only for few years is proving itself to be a good choice. Julia is a high-level, insanely fast and expressive language.
Julia is most suitable for working with the real-time streams of Big Data as its features are built on the core of the language. Julia’s ecosystem of extensions and libraries are not as mature or developed as the more established languages, but most popular functions are available, with more adding at a steady rate.
6. Pig Latin –
Pig Latin is among the best programming languages for data science which is also oriented with Hadoop and is also an open source system. It forms the Language layer of the apache Pig Platform, which sort and apply mathematical functions to large, distributed datasets.
Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark.
It can be extended by using the user defined functions which could be written in any language that is supported by it like Java, Python, JavaScript, Ruby or Groovy. A function call of these could be made directly from the code of Pig Latin language.
7. GO –
Go, was developed by Google in 2007 which is a free and open source programming language. Though being a new comer in the world of Data Science, it is gaining steam because of its simplicity. In the first place, Go was not developed for statistical computing but it soon got the mainstream presence because of its speed and familiarity.
Go’s syntax is based on C, which prove to be of great aid in its adoption. Go can also call routine programs, which are written in other programming languages like Python to achieve functionalities which are not accommodated in the Go.
The above list tells you about the best 15 data languages that you could choose for your Big Data Organization.
Well, with this we do come to an end of the Functional Layer Architecture, but not to the end of Big Data. Every day a new mystery is unveiled about Big Data. Even after learning about all the tools there is lot more left to know, understand, analyze, learn and accomplish in the Big Data.
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