Top 5 Data Science Programming Languages

There is a constant rise in the demand for skilled data scientists in the market. Without data, no business can develop and the data keeps increasing every day.

Therefore, it becomes highly important to properly access the data coming from all possible sources and work on it. The role of the data scientist is to use the tools and algorithms to process the data and bring out useful results.

According to Forbes, Data Science has been termed as the best job in the US. U.S. Bureau of Labor Statistics predicts there will be a 27.9 percent rise in employment in the field by 2026.

With rapid advancements in data science, 100 Days of Code challenges in data science are also becoming more popular.

In case you are planning to become a data scientist, you need to be proficient in any one of the data science programming languages. Thinking which language to learn from available coding languages?

Well, don’t worry, we have listed out the top 5 data science programming languages. Have a look at them below:

Data Science Programming Languages

1. Python

Python is the most popular programming language in the world of artificial intelligence and machine learning. It is an extremely powerful and dynamic programming language for data science.

Python is also the simplest language to learn and understand. The powerful libraries of Python make it the best statistical tool and a perfect suit for analysis.

2. R

R is another powerful modeling and visualizing programming language for data science. It is an open-source programming language with one of the biggest communities of learners.

In open statistical analysis, none of the tools can challenge R. There are many models available in R and recruiters keep looking for employees who have a combo of R and machine learning.

3. Spark

Apache Spark is an open-source tool that is responsible for handling the processing of real-time data with the help of Spark RDD, Spark SQL, and the machine learning libraries of spark.

High speed, ease to access, and use of high-level libraries are some of the benefits of spark. Learning Spark language is beneficial if you are willing to excel in the data science world.

4. SQL

In the field of data science, it is difficult to survive without SQL. SQL is very useful to fetch the data from RDBMS and edit as per the requirements. For decades, SQL is used by experts to get the data and store it safely.

The processing time is very fast and hence it is widely used for online transactions. Learning SQL is quite simple and Lynda provides the best SQL training.

5. Scala

Last on the list is SCALA i.e. scalable language. It is an open-source programming language and has a very huge user base.

The language operates on JVM and is best for organisations where data is available in every quantity. SCALA is the most popular data science programming language that works perfectly in combination with Hadoop.