Top Programming Languages ​​For Data Science

Top programming languages ​​for Data Science from Codingcompiler. Here we will discuss about their strengths and weaknesses when working with data science. Let’s start learning more on best data science programming languages.

Best Programming Languages For Data Science

1. R
2. Python
3. SQL
4. Java
5. Scala
6. Julia
7. Matlab
8. C++
9. Javascript
10. Perl
11. Ruby

Top Data Science Programming Languages

R Programming For Data Science

 

The R language was born in 1995 as the direct heir of the older language S. Created using C, Fortran and himself, R is supported by the R Foundation for Statistical Computing .

License: Free.

Pros:

• An excellent range of quality, specialized, open source packages. R has packages for almost any statistical application you can imagine: neural networks, nonlinear regression, phylogenetics, and much, much more.
• With a basic language setup, many statistical functions and methods are available. R also does an excellent job with matrix algebra.
• High-quality data visualization using libraries like ggplot2.

Cons:

• Performance, R is not the fastest language.
• The specificity of applications, R is great for statistics and data processing, but as a general purpose language it is hardly suitable.
• Its features, R has some unusual features that programmers who are familiar with other languages ​​may fall for. As an example: indexing starts from one, there are several assignment operators, data structures differ from traditional ones.

Python For Data Science

Guido van Rossum showed Python in 1991. Since then, it has become an extremely popular general purpose language that is widely used in data processing. The main versions of the language at the moment are 2.7 and 3.6.

License: Free.

Related Article: Learn Python in One Day

Pros:

• Python is very popular and has many extensions and support from the developer community.
• Python has a simple and easy-to-understand syntax – so it’s great for the role of a first programming language with a low input threshold.
• Packages like pandas, scikit-learn and Tensorflow make Python an excellent option for modern machine-learning applications.

Cons:

• Python is a language with dynamic typing. So you should be careful and expect from time to time errors like those where the method expects to receive an integer at the input, and receives a string.
• By the number of highly specialized packages for statistical analysis, Python loses R.

SQL For Data Science

SQL (Structured Query Language) is designed to define, manage, and query queries against relational databases. It appeared in 1974 and has undergone many changes since then, but its basic principles have remained the same.

License: Some implementations are free, others are proprietary.

Related Article: SQL Server Interview Questions

Pros:

• Very effective when working with relational databases.
• Declarative syntax makes SQL easy to read. It is quite clear what is meant by ‘SELECT name FROM users WHERE age> 18’.
• SQL is used in many applications, so it will be useful to become familiar with this language.

Cons:

• SQL analytical capabilities are rather limited. All that is available to you is the summation, and the calculation and derivation of the average value.
• For programmers who are used to imperative languages, declarative SQL constructs can be a nuisance.
• There are many implementations of SQL, for example, PostgreSQL, SQLite, MariaDB. They all vary enough to cause pain.

Java Programming For Data Science

Java is an extremely popular general purpose language. It uses the JVM (Java Virtual Machine) – its own abstract computing system, which provides full portability between different platforms. Supported by Oracle Corporation .

License: Java 8 is free, older versions are proprietary.

Related Article: Java Programming Tutorial

Pros:

• Java is omnipresent- Many modern systems and applications are created in Java.
• Strongly typed language- In relation to the definition of Java types is extremely serious. For applications that work with large amounts of data is invaluable.
• Java is a high-performance, general-purpose compiled language. The same language can be used for writing business logic and for analyzing large amounts of data, which other programming languages ​​for Data Science are not capable of.

Cons:

• For narrowly focused analysis and specific statistical applications, Java syntax is too verbose. Dynamically typed R and Python here will bring much more benefit.
• For Java, there are not so many libraries to work with statistics.

Scala For Data Science

The rock was designed by Martin Oderski and published in 2004. This is another language that the JVM uses to work. Scala is a multi-paradigm language that is able to implement both OOP and a functional approach.

License: Free.

Pros:

• Scala + Spark = high performance cluster computing. The ideal language for those who work with large data sets.
• Multi Paradigmatic – The developer is free to use both the PLO and the functional approach.
• Scala is compiled into Java bytecode and runs on a JVM. This allows Scala to interact with Java and, in principle, makes it a powerful general-purpose language.

Cons:

• Scala is not the easiest language to learn, so as the first one, it will hardly fit.
• The syntax in general and the typing system in the language are complex.

Julia For Data Science

He was born in 2012. The language was quickly adopted in the financial field.

License: Free.

Pros:

• Julia is compiled just-in-time, which provides good performance. It is also easy to learn and dynamically typed.
• Julia, like other programming languages ​​for Data Science, is intended for computing and analysis, but can also be used as a general-purpose language.
• Readability. Many users of this language refer to this plus as a key advantage.

Cons:

• Immaturity – Since the language has appeared recently, some packages may work unstable.
• The limited number of packages is another consequence of the youth of the language. In the future, Julia is sure to catch up, but for now R and Python gives this language a head start.

Matlab For Data Science

MATLAB is a recognized language for computing, used in academia and industry. Designed and licensed by MathWorks, a software company created in 1984.

License: Proprietary – the price depends on the application.

Pros:

• Created for calculations. Ideal for applications requiring complex math functions.
• It has a number of built-in functions for data visualization.
• Used in many university courses in physics, engineering and applied mathematics. As a result, it is widely used in these areas.

Cons:

• Proprietary license. The final cost, of course, depends on the application (there are home, student, academic or standard licenses), but you will have to fork out anyway (from $ 55 to a couple thousand).

Other Programming Languages ​​for Data Science

There are other general purpose languages ​​that are somehow suitable for working with data. We give them a brief overview.

C ++ For Data Science

Powerful general-purpose programming language with lightning performance. The issue of low C ++ popularity in Data Science is explained by the choice of computational productivity versus language performance.

Javascript For Data Science

Although with the advent of Node.js, JavaScript has become a serious server language, its use in Data Science is limited (although there is, of course, brain.js and synaptic.js). And the matter is that some of its shortcomings:

• Although Node.js is currently 8 years old, there are only a few libraries and modules for working with big data.
• Node.js is a rather productive platform, but JavaScript itself has many critics, and not without reason.

Node.js has asynchronous I / O – and this is a strong point. So, in the future, this may play in favor of JavaScript, as a serious language for handling large amounts of data. Another question is whether someone based on it will create what other programming languages ​​already have for Data Science.

Perl For Data Science

Perl has the fame of a Swiss knife among programming languages, thanks to its versatility, as a scripting language. It has a lot in common with Python and is a dynamically typed language.

However, in comparison with the same Python, it has very few extensions for working with data and there is not much enthusiasm in this area of ​​working with Perl. Perhaps the reason is not too friendly syntax.

Ruby For Data Science

Ruby is another popular dynamically typed general purpose language. However, it was also not adopted by developers working with big data in comparison with Python.

However, for Ruby there is a SciRuby project created for computing and data processing. However, for serious research it alone is not enough, so Ruby is not as popular as other programming languages ​​for Data Science.

Conclusion: So, what do you think on these top programming languages for data science? Share your views and insights in below comment section about which is the best data science programming language based on your working experience.

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