Java programming language still owns an eminent title in the data science world and it heralded as one of the top-most programming languages amongst employers and developers. Java is powerful, object-oriented, and flexible, one of the reasons why organizations still prefer this language.
Not to mention, Java boasts of having an unmatched community of developers that aids in learning. The larger the community is, the more support developers are likely to receive when using the tools and libraries. Java is said to have the second-largest community in StackOverflow – from front-end developers to back-end developers, and android developers. Besides this, the programming language is said to be the second most-tagged language in GitHub that consists of more than 1.5 million Java projects.
You will find that Java is used everywhere, from your smartphones to your web-embedded systems, and desktop systems Java is omnipresent. This is also one of the reasons why this programming language is gaining huge traction in the Internet of Things (IoT).
It has been more than 24 years since Java was created, and ever since, it has seen drastic evolution and growth.
You can also choose to enroll in a data science certification program to stay acquainted with how and why Java is used in data science.
- Java is easy and it is scalable
Java has always been a legacy language used by many developers across the globe, even before Python was developed. Developers find it easy to code using Java as their programming language and since it is an object-oriented language it possessesC/C++-like syntax which is familiar to all developers. Most companies use Java for the instant execution of their projects.
Since most of the projects a data science professional takes up are ambitious, it helps create applications that can be scaled later based on the requirement of the business. Java poses as an excellent choice for a company looking to build an application from scratch.
As a data scientist, you may find it easier to build complex applications using Java and yes, they’re easily scalable. For instance, Apache Spark is a great analytical tool used to build multi-thread applications and can be used for scaling.
- Java offers extensive frameworks
Other programming languages are beginning to find their place in the data science world, but remember Java was the first to get there. As a result, it possesses more tools for data science projects as compared to the other alternatives. Let us mention some of the popular machine learning and Java libraries here,
- Java-ML – the Java Machine Learning library is a collection of ML algorithms that have been implemented in Java. This algorithm helps in data manipulation, classification, feature selection, and clustering.
- Deeplearning4j–it is an open-source and a powerful tool for machine learning. It is a deep learning kit for Java and is used for the deployment of neural nets. Deeplearning4j can easily be integrated with Spark and Hadoop.
- Apache Mahout – Mahout is a scalable ML implementation that runs on the top of the Hadoop environment obtaining complete support of HDFS and MapReduce. Mahout stands to be a great component as compared to the other traditional ML tools such as Octave, Weka, and R.
- Kafka – Kafka is fault-tolerant, scalable, and is a publish-subscribe messaging system that helps in building distributed applications. It also powers up web-scale internet companies like Twitter, Airbnb, and LinkedIn.
- Hadoop – an open-source Java-based framework from Apache is specifically used to store, process, and analyze data in huge volumes.
- High speed
In terms of speed, Java is fast thus allowing multiple computations to take place at any point of time. It is said that Java is 25 times faster as compared to Python.
Besides speed, building an application with the help of Java takes lesser time as compared to the other programming languages. It makes use of a business-specific tool in development along with features and IDE that aids in creating large-scale business applications. However, being a senior data scientist you still have the choice of choosing the best programming language that is best suitable for your project.
- Possess unique syntax
Java’s syntax is easy and understandable by developers from across the globe. The syntax offers better readability from other programming languages such as C or C++. It has a rich API, one of the reasons why building sound, graphics, and writing games becomes much easier with Java.
- Java with Online transaction processing (OLTP) systems
Data warehousing along with OLTP systemsutilizes the mainframe systems during batch processing. As compared to other programming languages, Java stands a higher probability of being able to bond into that architecture. You can also integrate Java along with COBOL and any middleware software.
Java is a go-to programming language for companies that are seeking to invest in applications that deal with data analysis.
Consider learning Java if you’re looking forward to host data science functionalities like data processing, data visualization, data analysis, and statistical analysis.
Although Java is an old language it is still here to stay.
