As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language. R: Although it has a steep learning curve, it has various advantages. The big open-source community that provides R with high-quality open source packages. Includes loads of statistical functions and handles matrix operations smoothly. Via ggplot2, R provides us with a great data visualization tool. Python: Though it has fewer packages than R, Python is still one of the most sought after languages in the data science field. Pandas, scikit-learn, and tensorflow provide with most of the libraries needed for data science purposes. Easy to learn and implement it. It has a big open-source community as well. SQL: SQL is a structured query language which works upon relational databases. Pretty readable syntax. Efficient at updating, manipulating and querying data in relational databases. Java: Even though it has less number of libraries for data science purposes and with Java’s verbosity limiting its potential, it has many advantages as well: Compatibility. Systems are already coded in Java in the backend, and its therefore easier to integrate Java data science projects to it. It is a high-performance, general purpose, and a compiled language. Scala: Scala runs on JVM and has a complex syntax. Still, it is a preferred language in data science domain due to the following advantages: As it runs on JVM, any Scala program can run on Java as well. When used along with Apache Spark, we get high-performance cluster computing.