Top Programming Languages for Data Science

Top Programming Languages for Data Science

  • In today’s highly competitive market, which is anticipated to intensify further, the data science aspirants are left with no solution but to up skill and upgrade themselves as per the industry demands. Prevailing situation odes the mismatch between demand and supply ratio of data scientists and other data professionals in the market, which makes up a great age to grab better and progressive opportunities. The knowledge and application of programming languages that better amplify the data science industry, are must to have.
  • Therefore, here we have compiled the list of top data science programming languages for 2020 that aspirants need to learn to improve their career.  

Top Programming Languages for Data Science 

  1. Python 
  2. SQL 
  3. MATLAB
  4. Java
  5. Scala
  6. Julia
  7. Perl

1. Python

  • Python is one of the best programming languages for data science because of its capacity for statistical analysis, data modeling, and easy readability. Another reason for this huge success of Python in Data Science is its extensive library support for data science and analytics. There are many Python libraries that contain a host of functions, tools, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data mining, neural networks, data visualization, and so on. For example, Pandas is a free Python software library for data analysis and data handling, NumPy for numerical computing, SciPy for scientific computing, Matplotlib for data visualization, etc.

2. R

  • R is a very unique language and has some really interesting features which aren’t present in other languages. These features are very important for data science applications. Being a vector language, R can do many things at once, functions can be added to a single vector without putting it in a loop. As the power of R is being realized, it is finding use in a variety of other places, starting from financial studies to genetics and biology and medicine.

3. SQL

  • SQL or Structured Query Language is a language specifically created for managing and retrieving the data stored in a relational database management system. This language is extremely important for data science as it deals primarily with data. The main role of data scientists is to convert the data into actionable insights and so they need SQL to retrieve the data to and from the database when required. There are many popular SQL databases that data scientists can use such as SQLite, MySQL, Postgres, Oracle, and Microsoft SQL Server. BigQuery, in particular, is a data warehouse that can manage data analysis over petabytes of data and enable super fats SQL queries.

4. MATLAB

  • MATLAB is a very popular programming language for mathematical operations which automatically makes it important for Data Science. And that’s because Data Science also deals a lot in math. MATLAB is so popular because it allows mathematical modeling, image processing, and data analysis. It also has a lot of mathematical functions that are useful in data science for linear algebra, statistics, optimization, Fourier analysis, filtering, differential equations, numerical integration, etc. In addition to all these, MATLAB also has built-in graphics that can be used for creating data visualizations with a variety of plots.

5. Java

  • Java is one of the oldest languages used for enterprise development. Most of the popular Big Data frameworks/tools on the likes of Spark, Flink, Hive, Spark and Hadoop are written in Java. It has a great number of libraries and tools for Machine Learning and Data Science. Some of them being, Weka, Java-ML, MLlib, and Deeplearning4j, to solve most of your ML or data science problems. Also, Java 9 brings in the much-missed REPL, that facilitates iterative development.

6. Scala 

  • Scala is a programming language that is an extension of Java as it was originally built on the Java Virtual Machine (JVM). So it can easily integrate with Java. However, the real reason that Scala is so useful for Data Science is that it can be used along with Apache Spark to manage large amounts of data. So when it comes to big data, Scala is the go-to language. Many of the data science frameworks that are created on top of Hadoop actually use Scala or Java or are written in these languages. However, one downside of Scala is that it is difficult to learn and there are not as many online community support groups as it is a niche language.

7. Julia

  • Julia is an open-source programming language that is also an accessible, intuitive, and highly efficient base language with a speed that exceeds R and Python. This makes Julia a formidable language for data science. Along with speed and ease of use, it has more than 1900 packages available. Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran.

8. Perl

  • Perl can handle data queries very efficiently as compared to some other programming languages as it uses lightweight arrays that don’t need a high level of focus from the programmer. It is also quite similar to Python and so is a useful programming language in Data Science. In fact, Perl 6 is touted as the ‘big-data lite’ with many big companies such as Boeing, Siemens, etc. experimenting with it for Data Science. Perl is also very useful in quantitative fields such as finance, bioinformatics, statistical analysis, etc.

 

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