Data Science is a vast field which requires working with a large number of libraries. Finding the right programming language to master is, therefore, important for efficient working with all the libraries-
R programming: The only challenge of R is its steep learning curve, but it is an important language for various reasons
- It has a huge open-source community that provides numerous high quality open-source packages for R.
- It boasts of smooth handling of matrix operations and has large statistical functions.
- It has ggplot2 that enables data visualization
Python: With lesser packages than R, Python is still considered to be popular with data scientists. The reasons for that is-
- Libraries like pandas, scikit-learn and tensorflow equip Python to provide most library needs for data science purposes.
- It is very easy to use and operate.
- It has an open-source community that is considered one of the largest one.
SQL: Working on relational databases, Structured Query Language has-
- Readable syntax
- Efficiency in updating, manipulating and querying data for relational databases.
Java: One of the oldest programming languages, Java has limited libraries limiting its potential. Nevertheless it has some advantages.
- Systems coded with Java at the backend makes it easier to integrate data science projects with it making it a compatible option.
- It is a high performance, general purpose, compiled language.
Scala: Working on JVM, it is considered rather complicated. But it does have some advantages:
- Running on JVM, Scala can run on Java as well.
- Used alongside Apache Spark it enables high performance computing cluster.