Java is the #1 programming language for Big Data, Analytics, DevOps, and AI. It is consistently the first choice for developers working with data. The platform-independent programming language is robust, scalable, and reliable. Finding uses in data science, Java development services are in high demand among companies that are focusing on utilizing data for enterprise expansion and growth.
21% of data scientists use Java application development on a regular basis. It is the 5th most popular programming language for data, just after Python, SQL, R, and C/C++. While many developers use Python and R for Machine Learning applications, knowing Java is essential for data scientists as well. It has great uses in Machine Learning and Artificial Intelligence.
Java is mostly used to put Machine Learning models into production. Even though Python is the most popular programming language for Machine Learning, it cannot easily engage in model production and is slow when it comes to execution. Therefore, Java can be advantageous to data scientists who frequently execute ML models.
Read more: Top 10 Popular Java Applications Examples in Real-world
This article will address the top 7 reasons why data scientists should know Java. It will highlight how Java software development services enable enterprises and startups to take advantage of its versatility in Machine Learning development.
7 reasons Java is Good for Data Science
Some of the world’s best companies use Java, including Uber, Spotify, Airbnb, Wikipedia Search and more. It offers a plethora of services that developers can build using different IDEs and integrate Machine Learning models in them. Data Science is one field that requires a lot of heavy lifting, which means it needs a programming language that survives that. Java programmers build the most complex applications with ease.
Here are 7 reasons why data scientists should know Java application development –
- Data Science Frameworks
Any Java development company would attest to the fact that the programming language has great frameworks for Machine Learning development. Java engineers admire the technology because it offers them complete flexibility and simplicity through the amazing frameworks.
There’s DeepLearning4J for neural network development, ND4J for scientific computing and signal processing, and Apache Mahout for classification, clustering, and regression models.
Hadoop and Kafka are two of the most popular frameworks that any Java software development company would use to handle data intensive applications.
- Scalable Development
Java is the long standing champion in building scalable applications. Data science has heavy requirements, and model deployment requires a powerful programming language. Java has the capability to scale Machine Learning applications with ease.
There are over 45 billion active Java Virtual Machines around the globe. Java developers utilize them to work with high-end software and systems. Since Machine Learning requires simultaneous request processing, Java is a good choice.
There are lots of libraries and plugins that programmers utilize to build the application and execute Machine Learning algorithms.
- Easy to Read & Write
The most important part of custom software development services for Machine Learning is that the programming language should be easy to read and write. The simplicity of Java programming makes it easier for developers to code the Machine Learning model and run algorithms.
Any beginner developer who joins the project easily understands what’s going on in the data model. Since it is a legacy application, it is useful majorly in complex applications, which provides engineers the experience to understand it easily.
On top of that, Java programmers are easily available in the market. The programming language has been around for years, so the supply of engineers is high.
- Java Virtual Machine
The Java Virtual Machine is an ecosystem that enables developers to write code on multiple platforms. There are a lot of IDEs that allow enterprises to create applications on different operating systems and improve developer productivity. Java is a legacy language that developers use for building applications that are efficient.
Machine Learning services require high performance, which programmers can achieve through Java. Along with the Hadoop ecosystem, JVMs are an amazing environment to work with data and analytics.
Using JVM, developers can also create tools quickly. Therefore, any Machine Learning model that requires distinct features and tools development can use Java.
- Faster Development
Java is said to be 25 times faster than Python. The programming language can do tons of computations at a single instance, which means that Machine Learning models that require heavy lifting can work extremely well with Java.
The processing speed of Java is also unmatched as compared to other programming languages. There’s a lot of things that Java can process without any hassle.
On top of that, Java development is fast in itself. Companies can build products with ease and without any problems. There are lots of tools for creating large scale enterprise applications.
- Algorithm Deployment
Java facilitates the development and deployment of algorithms with ease. Therefore, programmers who know both Java and Python are more likely to get hired by companies than anyone else. They have a lot of flexibility and versatility in creating systems based on Machine Learning.
The codebase for Java offers a high amount of integration as well. They can easily connect the algorithm to the codebase and new developers can begin to allocate the code without any hassle.
Deploying algorithms in Java is easy because the programming language has a simple syntax.
- Wide Community
One of the major reasons data scientists should know Java programming language is because it has a wide community. If any data scientist needs help with documentation or resources, they can easily get it as Java is one of the most developer-friendly programming languages.
On top of that, they can get support from the community for building Machine Learning applications and deploy each other on different projects. The community is growing by the day.
The best thing is that the community adds a lot of things to Java. There are upgrades and updates that enable data scientists to make use of the best features and build comprehensive Machine Learning solutions.
Wrapping Up
Java is an amazing programming language for data scientists due to its scalability, versatility, and flexibility. There are a lot of features and tools that they can use to build Machine Learning models and deploy them with ease. Any Java development company can create Machine Learning solutions by using the tools & technologies that the programming language has to offer.
