With so many programming languages to choose from, it can be difficult to decide which one to focus on or learn in order to increase your skills within the data science industry. While most people focus on the most popular programming languages across fields, it is also important to analyze which languages are the most relevant for the career and industry that you plan on pursuing. Known as the world’s most popular programming and development platform, Java has multiple functions and capabilities which make it a useful skill to learn for data science students and professionals. This article will discuss the Java programming language and how Java is used within the world of data science.

Background and Uses of Java

Created in the 1990s by Sun Microsystems, Java is known as one of the most lucrative programming languages to learn because of its ubiquity across applications and digital platforms. Known as an object-oriented programming language, working with Java is set apart from other languages due to its syntax and methods of writing code. Made to run on any Java-compatible machine, the Java programming language is a go-to for working with big data that is held in virtual spaces, like the cloud. Java is also used with some of the top social media platforms and digital technologies, such as Twitter and Netflix, making it a well-known name for those that are working or seeking employment in technology companies and corporations.

Due to its popularity within web development and design, Java is commonly used for web based applications and mobile technology, such as Android devices and Google documents. Although knowledge of the Java programming language is most commonly required within Java specific positions, such as becoming a Java Developer, Java is also useful within the realm of data science. Data science students and professionals can learn Java for its capabilities as an object-oriented programming language with cloud capabilities, that offers many valuable frameworks and functions which have been compiled by a community of Java users, as well as the capacity to complete more complex big data projects.

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Object-Oriented Programming

As an object-oriented programming language, learning Java requires an understanding of the unique characteristics and categories of working in this style. Object-oriented programming languages are based on the relationships between objects which have their own unique fields and data types. Although this category of programming is not as common within the world of data science there are several aspects of learning an object-oriented programming language like Java that is especially useful within the industry.

Due to the fact that writing code in an object-oriented programming language is highly reusable, when working with complex data science processes such as machine learning and automation, it is easier and more efficient to use this type of programming for repetition and iteration. In addition, when working on a data science team or collaboration, the Java programming language makes it easier to share code with other Java programmers that can easily be run on another machine without having to modify the code, saving time and money.

Oracle & Cloud Computing

Of the many products that can be used with Java, the use of Oracle software is a standout aspect of learning this language. In 2010, Oracle procured Sun Microsystems (the parent company of the Java programming language) which has resulted in Java being one of the many products offered through Oracle. Oracle has worked to create multiple applications and software packages that are compatible with the Java programming language and useful for completing and collaborating on data science projects.

There are several Java technologies at Oracle, of which one of the most widely used is the Java Virtual Machine or JVM, which allows anyone with the software to run Java programs on their own computer. This makes it easier to work on data science projects within a team based environment as you can write code one time and run it on multiple machines i.e. The “Write once, run anywhere” philosophy of Java programming. There are also Java compatible database management systems and microservices which offer cloud computing services that make it easier to create code for applications and machine learning projects.

Data Science Frameworks and Libraries

In addition to its uses as a cloud based object-oriented programming language, Java includes multiple frameworks and functions that can be used by data science students and professionals. Similar to data science packages and libraries, frameworks include resources, interfaces, and other tools that simplify the process of completing and running a program or code. Many times frameworks are utilized for automation and machine learning in order to better manage and manipulate data. While there are many data science focused frameworks in Java, some of the most popular frameworks that incorporate the Java programming language are Apache Hadoop, Mahout, and Deeplearning4J.

As one of the worlds’ largest open-source software foundations, Apache includes multiple libraries, projects, and initiatives which are regularly updated by a community of active volunteers. Within the Apache community, Hadoop is a software library and framework which gives data science students and professionals the ability to work on large scale projects across a network or clusters of computers. In addition, Apache Mahout can also be used to scale data science projects and is known for its machine learning and mathematical capabilities. Outside of the Apache software community, Deeplearning4J is another open-source Java library that is used for deep learning projects and developing neural networks.

Big Data Projects and Processes

Java is also known for its speed and efficiency when completing projects at scale, which means that the programming language is especially useful for big data projects that require some knowledge of machine learning and artificial intelligence. Big data is defined as any type of data that is not only large in size, but that also includes a high volume and variety of different types of data. At this current moment in time, big data projects are viewed as the future of data science, as more institutions and companies focus on collecting large amounts of data each and every day. The Java programming language is an excellent option for programmers and data science students and professionals that are interested in going into an industry where big data is used, such as science and technology, social media, and web development and design.

Want to learn more about using Java for Data Science?

As a fundamental skill for data science students and professionals that are interested in object-oriented programming, Noble Desktop offers several Java classes and a Java Bootcamp which focuses on teaching all of the different ways that you can use the Java programming language. The Java Bootcamp also includes training to take the Java SE Programmer exam, which is especially useful if you are interested in pursuing a career in data science or web development. In addition to Java bootcamps and courses, Noble Desktop also offers live online data science classes and certificate programs which teach multiple programming skills. There are also in-person data science classes located in a city near you that focus on teaching programming languages through bootcamps and immersives.