Most Popular Programming Languages For Data Science

It has been seen that more data is produced day by day over the last several years. In such a situation, data science plays an important role in processing and analyzing the data to get valuable insights. To get exact and effective analysis, data scientists use various programming languages for data science. Several programming languages are useful for data science, but all of them can be used as per the situation and purposes. We have listed the most popular programming languages for data science. Get all the relevant and detailed information about each programming language that is useful for data science.

List of various programming languages for data science

Python

Python is considered to be one of the popular programming languages for data science as it has statistical analysis capacity, easy readability, and data modeling features. Moreover, it offers numerous libraries that support data science. These libraries are used for different purposes, such as NumPy used for numeric computations, Matplotlib used for data visualization, Pandas for data handling and data analysis, and much more. Being an OOP language, python supports several paradigms. Besides this, there are several reasons why data scientist must learn python for data science, and these are:

• Python is an open-source language,
• It is interpreted,
• Python is high-level programming,
• It has an immense community and much more.

Scala

Scala refers to Scalable language. It is a programming language, which is Java’s extension because it was built over a JVM (Java Virtual Machine). Therefore, it has been seen that it easily gets integrated with Java. The basic reason why Scala is one of the best programming languages for data science is that it utilizes Apache Spark that helps in managing a huge amount of information. Therefore, it will be correct to say that if you have large data to manage, just go for Scala. There are several reasons to learn Scala for data science, and these are:

• Scala is a general-purpose, multi-paradigm, and high-level programming language.
• Being an OOP language it supports various functional programming approaches.
• Scala contains excellent features of various languages, such as C++, C, Java, and much more, making it more productive and useful for data scientists.

Julia

Julia is the preferred programming language for data science as it works faster than R, MatLab, Python, and JavaScript. Moreover, it is a great language for numeric analysis as it has various mathematical libraries and different data manipulation tools. Julia can easily integrate with different programming languages, such as Python, Matlab, C++, Fortran, and much more. There are the following reasons to learn Julia for data science, and these are:

• It is an open-source language, which means all source codes are accessible easily.
• Julia needs fewer lines of code that make it flexible to use.
• Julia has the JIT (just-in-time) compiler feature that makes it much faster to execute extensive data.

Matlab

Matlab is another programming language that is used for numeric computations that makes it essential for Data Science. It has mostly used for mathematical modeling, data analysis, and image processing. Numbers of mathematical functions, such as sqrt(x), are used in data science for statistics, Fourier analysis, differential equations, linear algebra, optimization, filtering, numerical integration, and much more. Moreover, Matlab has built-in graphics, which is useful for presenting data visualizations with the help of various plots. There are numerous reasons why students must learn Matlab for data science, and these reasons are:

• Rich Matlab libraries,
• Suitable for matrix calculations,
• Need fewer lines of code, and much more.

R

R considers to be one of the best programming languages for data science because it was developed for statistics solutions. It gains popularity day by day because of its active communities and libraries available to use. R offers libraries, which contain a host of tools, functions, and ways to analyze and manage information. Moreover, each library focus on a different function, such as data manipulation, web crawling, managing textual data and image, data visualization, and much more. Why R is useful for data science:

• RStudio has an IDE, which enhances the graphics accessibility and involves a syntax-highlighted editor, which supports fast execution of code.
• Because of the wide variety of the R library, this programming language is suitable for graphical and statistical usage. 
• Companies like Google, bing, Wipro, Facebook, Accenture, and several other companies using R for data science.

Java

It is known to all that Java is one of the oldest programming languages for data science. Several data sciences and big data tools are running on the Java programming language, like Spark, Hadoop, and Hive. There are various data science libraries available in Java, like MLib, Deeplearning4j, Weka, Java-ML, and much more. There are various reasons for using the Java programming language for data science, such as:

• Java offers numerous toolsets for data science as well as machine learning. 
• Java Virtual Machine has Scala, which makes it useful for large data analyzes.
• It offers a high speed that makes it more reliable to design large-scale applications and fast data analyses.

SQL (Structured Query Language)

This is particularly design for retrieving and managing the data that is stored within the relational DBMS. The main objective of data scientists is to change the information into an achievable action. That is why they require SQL to get the information from the databases. Several renowned databases are SQLite, Postgres, Microsoft SQL Server, MySQL, Oracle, and much more. There is a significant role of the SQL in data science, and data scientist use it because:

• It is very effective at manipulating, updating, and querying relational databases.
• Because of declarative syntax, SQL is an easily readable language.
• SQL modules, like SQLAlchemy, helps SQL to be easily integrated with another language.

Conclusion

Now, you get detailed about which are the most popular programming languages for data science. So, it would be beneficial to go ahead and try to practice them as much as possible. Every programming language is not always correct for data science as all have their own importance, such as SQL used for data management and python for data analysis. Therefore, we can say that it’s all up to you which language you want to use and for which purpose. You just need to remember that whatever your preferences would help you expand the skillsets and support you to become a Data Scientist!

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