Why should data scientists know about Java?
Data scientists need to learn Java because it provides a host of data science features such as data analysis, data processing, statistical analysis, data visualization, and natural language processing (NLP).
Java is a unique, versatile, object-oriented language that offers tons of functionality.
Its excellent performance and speed make it one of the most sought-after skills in the market. It also provides security capabilities, network centric programming, and platform independence.
Source: Pearson Frank
Java is very versatile because it is used to program applications on the web, mobile devices, and desktops using different platforms. Besides, Java has many features like dynamic encoding, multiple security features, platform independent features and network centric design that make it very versatile.
Java is one of the most demanded programming languages in use today.
As a data scientist, you must know how to use Java because it offers a multitude of other services to create a business application.
Many large companies like Uber, Spotify, and Airbnb are Java-based.
Here are many reasons why data scientists should learn Java:
1. Java is easy to understand
2. Java is free
3. Java has many great frameworks for data science
4. Java has excellent scalability
5. Java has excellent documentation support
6. Java has a plentiful API
Data science is, ultimately, the application of programming to stochastic methods and probability theory. Statistics and probability theory require specific data structures to be calculated and, as such, are largely independent of any programming language. Statistics oriented packages like R or Python Pandas both provide a pretty decent set of tools for working with these algorithms, with Scala (especially for Spark) becoming a more commonly used tool.