# Top Programming Languages for Data Science in 2020

There is more data being produced daily these days than there was ever produced in even the past centuries! In such a scenario, Data Science is obviously a very popular field as it is important to analyze and process this data to obtain useful insights. But now the question is **“Which language to use for Data Science?”**. There have been a lot of debates between Python and R and which of them is more popular for data science! However, both of those languages are equally important and valid choices for any data scientist. Apart from them, there are also other programming languages that are important in data science and can be used according to the situation.

This article compiles all these **top programming languages for Data Science**. All of these languages have their own pros and cons and are uniquely suitable depending on the scenario. So let’s check out these languages along with Python and R that are of course the most popular and remain the all-time favorites for data science!

### 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

When talking about Data Science, it is impossible not to talk about R. In fact, it can be said that R is one of the best languages for Data Science as it was developed by statisticians for statisticians! It is also very popular (despite getting stiff competition from Python!) with an active community and many cutting edge libraries currently available. In fact, there are many R 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 manipulation, data visualization, web crawling, machine learning, and so on. For example, dplyr is a very popular data manipulation library, ggplot2 is a data visualization library, etc.

### 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 programming languages and it is pretty important in data science as well. Most of the big data and data science tools are written in Java such as Hive, Spark, and Hadoop. Since Hadoop runs on the Java virtual machine, it is important to fully understand Java for using Hadoop. Moreover, there are many Data science libraries and tools that are also in Java such as Weka, MLlib, Java-ML, Deeplearning4j, etc.

### 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 extremely fast programming language and it can work with data even faster than Python, R, MATLAB, or JavaScript. Julia is also great for numerical analysis which makes it an optimal language for data science. Julia has mathematical libraries and data manipulation tools that are a great asset for data analytics but it also has packages for general-purpose computing. All in all, Julia has a total of 1900 packages available. It is also able to integrate with other programming languages like R, Python, Matlab, C, C++ Java, Fortran, etc. either directly or through packages.

### 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.

### Conclusion

Now that you know the top programming languages for data science, its time to go ahead and practice them! Each of these programming languages has its own importance and there is no such language that can be called a “correct language” for Data Science. For example, you may use Python for data analytics and also SQL data management. So, it is upon you to make the correct choice of language on the basis of your objectives and preferences for each individual project. And always remember, whatever your choice, it will only expand your skillset and help you grow as a Data Scientist!