Top 7 Programming languages to perform Data Science in 2020

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Top 7 Programming languages to perform Data Science in 2020

Data science is an exciting field to work, combining advanced statistical and quantitative skills with real-world programming capabilities. There are many programming languages ​​in which an aspiring data scientist may consider specializing.

Let's take a look at some of the most popular languages ​​used in data science:

1. R Programming language:

Launched in 1995 as a direct descendant of the old programming language S, R has been strengthening. R is a powerful language that excels in a wide variety of data visualization and statistical applications, and being open source allows for a very active community of taxpayers.


  • Excellent range of open source and high quality packages. R has a package for almost every quantitative application and imaginable statistics.
  • The basic installation comes with comprehensive statistical functions and methods. R also handles matrix algebra particularly well.
  • Data visualization is a key strength with the use of libraries like ggplot2.

2. Python:

Python is a very good language option for data science, and not just at the input level. According to Data science training institute in Bangalore, the data science process revolves around the ETL process which makes the generality of Python fit perfectly.


  • Python is a very popular and general purpose programming language. It has a wide range of specific modules and community support.
  • Python is an easy language to learn. The low entry barrier makes it a first language, which is ideal for those who are new to programming.
  • Packages such as pandas, scikit-learn and Tensorflow make Python a solid choice for advanced machine learning applications.

3. SQL:

Much of the information science process depends on ETL, and the longevity and efficiency of SQL are proof that it is a very useful language for the modern data scientist.


  • Very efficient in queries, updating and manipulation of relational databases.
  • Declarative syntax makes SQL a very readable language. There is no ambiguity about what to do
  • SQL used in a wide range of applications, making it a very useful language to be familiar with. Modules like SQLAlchemy make the integration of SQL with other languages ​​simple.

4. Java:

Java is an extremely popular language that runs on the Java Virtual Machine (JVM). It is an abstract computer system that allows perfect portability between platforms. Many companies will appreciate the ability to integrate the data science production code directly into the basis of an existing code, and we also find that Java performance and type security are very advantageous.


  • Ubiquity. Many modern systems and applications are based on a Java backend. The ability to integrate data science methods directly into the existing code base is powerful.
  • Strongly typed. Java is a good language when it comes to ensuring type security. For mission-critical big data applications, this is very important.
  • Java is a compiled language of general purpose and high performance.

5. Scala:

Developed by Martin Odersky and released in 2004, Scala is a language that runs on the Java Virtual Machine (JVM). It is a multi-paradigmatic language, which allows both object-oriented and functional approaches. The Apache Spark cluster computing framework is written in Scala. However, if your application does not handle data volumes that justify the added complexity of Scala, your productivity is likely to be much higher when using other languages, such as R or Python.


  • The term scala spark defines high performance cluster computing. Scala is an ideal language for those who work with large volume data sets.
  • Scala is compiled in the Java bytecode and runs in a JVM. Making Scala a very powerful general purpose language, as well as being suitable for data science.

6. Julia:

Launched in 2011, Julia impressed the world of numerical computing. His profile was raised by early adoption by several important organizations, including many in the financial industry.  As a recent language, it is not as mature as its main alternatives: Python and R.


  • Julia is a JIT (' just-in-time ') compiled language, which allows her to offer good performance. It also offers the simplicity, dynamic typing and scripting capabilities of a language interpreted as Python.
  • Julia was specifically designed for numerical analysis. But it also offers general purpose programming.
  • Readability. Many language users mention this as a key advantage.


MATLAB is a numerical computing language that is used in academia and industry. Developed and licensed by Math Works, a company established in 1984 to market the software. The widespread use of MATLAB in a variety of quantitative and numerical fields both in the industry and in the academic world makes it a serious choice for data science.


  • Designed for numerical computing. MATLAB is suitable for quantitative applications with sophisticated mathematical requirements, such as signal processing, Data visualization MATLAB have built-in large plotting capabilities.
  • MATLAB is frequently taught as part of undergraduate courses in quantitative subjects such as Physics, Engineering and Applied Mathematics. As a consequence, it is widely used in these fields.


Well, we have seen a quick guide on what languages ​​to consider for data science. The key here is to understand the usage requirements in terms of generality versus specificity, as well as your preferred development style of performance versus productivity.

For a GIS technician who wants to start performing data science, the ideal is to use R, Python or SQL. Since the most common functions will be to develop existing data processes and ETL processes. These languages ​​provide an adequate balance between generality and productivity, with the option of using more advanced R statistical packages when necessary.