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Java for Data Science: Tools, Importance, When & How to Use?

18th Jun, 2024
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    Java for Data Science: Tools, Importance, When & How to Use?

    In recent years, Machine LearningArtificial Intelligence, and Data Science have become some of the most talked-about technologies. These technological advancements have enabled businesses to automate and operate at a much higher level. 

    In recent years, quite a few organizations have preferred Java to meet their data science needs. From ERPs to web applications, Navigation Systems to Mobile Applications, Java has been facilitating advancement for more than a quarter of a century now. Java, originally recognized for its portability, scalability, and object-oriented programming paradigm, has expanded its reach beyond traditional software development into the realm of data science using Java. With a rich ecosystem of libraries, frameworks, and tools, Java provides a solid foundation for developing data-centric applications, handling big data processing, and deploying machine learning models. 

    In this blog, we are going to explore how Java for Data Science is a great option to have. We will also discuss how Java frameworks, scalability, syntax, and processing speed can be crucial when you develop projects in data science using Java. So let us get to it.

    Importance of Java for Data Science 

    When it comes to data science, Java delivers a host of data science methods such as data processing, data analysis, data visualization statistical analysis, and NLP. Java and data science allow applying machine learning algorithms to real-world business products and applications. Data Science,  Artificial Intelligence, and Machine Learning are tempting big money today. So if you can program in Java, you know you have an important skill. Hone your Java skills and use them for data science with our Data Science training that includes dedicated tutorials on data science for Java developers as well.

    Why Use Java for Data Science?  
    Java features
    What is Java - Java Platforms

    • Java is gaining traction in the realm of data science due to several compelling reasons. Firstly, its inherent portability ensures that Java applications can seamlessly run across diverse computing environments, making it an ideal choice for developing data solutions that need to be deployed on various platforms. 
    • The integration of Java with the Apache Hadoop ecosystem is a key factor. Hadoop, a widely-used framework for distributed storage and processing of large datasets, relies on Java for implementation. This integration empowers data scientists to harness the scalability and performance of Java when dealing with extensive datasets and distributed computing. 
    • The Java Virtual Machine (JVM) further enhances its appeal by offering platform independence. Applications written in Java can run on any device with a compatible JVM, simplifying deployment and maintenance. Moreover, Java's strong support for object-oriented programming and its mature ecosystem of libraries and frameworks contribute to efficient data manipulation, analysis, and machine learning tasks.
    • Java is a language with a huge community in words   
    • Java is easy for almost all programmers to split functionality   
    • Java is strongly typed   
    • Java programming permits programmers to be explicit about data and types of variables data they deal with. This is helpful in the era of data science and data management machine learning. 
    • The suite of mechanisms for Java is rather well developed. A range of mature elements and IDEs allow developers to be well productive. 
    • The Java Virtual Machine (JVM) is especially good for documenting code that looks matching on multiple platforms and it works well the big data space. 
    • Scala is being used in machine learning technologies and big data processing tools like Apache Spark. Scala is built on the JVM and performs rather well with Java as compared to Scala. 
    • Decisions connected to programming language preference are made to reduce programming, features, code training maintainability and tooling. 
    • In summary, Java's portability, integration with big data frameworks like Hadoop, JVM advantages, and robust ecosystem make it a pragmatic choice for data scientists aiming for scalable, platform-independent solutions in the dynamic landscape of data science. 

    Java Tools and Frameworks for Data Science  

    In order to stay relevant in the space of digital transformation, we suggest "selecting the right machine learning tool". Java offers a range of tools and frameworks that facilitate various aspects of the data science workflow, from data manipulation to machine learning. Here are some notable Java tools and frameworks for data science: 

    1. Weka 

    Description: Weka is a collection of machine learning algorithms for data mining tasks. It provides a graphical user interface for data preprocessing, classification, regression, clustering, and more. 

    Website: Weka 

    2. Apache Mahout 

    Description: Mahout is an Apache project that focuses on scalable machine learning algorithms. It provides implementations of various recommendation, clustering, and classification algorithms. 

    Website: Apache Mahout 

    3. Deeplearning4j 

    Description: Deeplearning4j is a deep learning framework for Java. It supports building and training deep neural networks and is designed for use in business environments with support for parallel processing and distributed computing. 

    Website: Deeplearning4j 

    4. Apache Spark 

    Description: While primarily written in Scala, Apache Spark has extensive support for Java. Spark is a fast and general-purpose cluster-computing framework that provides high-level APIs for distributed data processing, including machine learning. 

    Website: Apache Spark 

    5. DL4J (Deep Learning for Java) 

    Description: DL4J is a deep learning library for Java and Scala. It integrates with Hadoop and Spark and provides support for various neural network architectures. 

    Website: DL4J 

    6. RapidMiner 

    Description: RapidMiner is a data science platform that offers a visual workflow designer for building and deploying data science solutions. It supports various machine learning algorithms and data preprocessing techniques. 

    Website: RapidMiner 

    7. H2O.ai 

    Description: H2O.ai provides an open-source platform for building machine learning models. H2O.ai's platform supports various algorithms and can be used for tasks such as classification, regression, clustering, and anomaly detection. 

    Website: H2O.ai 

    8. Mallet 

    Description: Mallet is a Java-based machine learning toolkit for natural language processing tasks, including document classification, clustering, topic modeling, and information extraction. 

    Website: Mallet 

    Here are some more listing of the Java and data science tools that would help you to keep a suitable interface to the production stack.

    • DL4J for Deep Learning  
    • ADAMS for Advanced Data Mining   
    • Java for Machine Learning Library  
    • Neuroph for Object-Oriented Artificial Neural Network (ANN)   
    • RapidMiner for machine learning workflow  
    • Weka for Waikato Environment for Knowledge Analysis 

    These tools and frameworks demonstrate the versatility of Java in the field of data science, providing solutions for tasks ranging from traditional machine learning to deep learning and big data processing. Depending on the specific requirements of a data science project, one or more of these tools can be selected to streamline the development and deployment of data-driven solutions. If you are also interested to learn these latest tools and technologies enroll for this amazing Data Engineering Bootcamp now. 

    Advantages of Java in Data Science

    1. Java is Easy To Understand

    Java is based on object-oriented programming, as a result it stays popular among programmers. While Java cannot be as easy as Python, it is fairly beginner-friendly and easy to understand.

    J2. ava Offers Scalability to Your Data Science Applications

    Java for data science is perfect when it comes to scaling your products and applications. This makes it a wonderful choice when you’re considering building extensive and more complex ML/AI applications. If you are just starting out to create your products from the scratch, it is a good idea to choose Java as your programming language.

    3. Unique Syntax in Data Science Using Java

    Java programmers are usually clear about the data types, variables, and data sources they deal with. It makes it easy for them to retain the code base and skip documenting trivial unit tests cases for products and applications. Java 8 included Lambda expressions, which corrected most of Java’s rambling, thus making it less distressing to develop large business/data science tasks. Java 9 gets in the much-missed REPL, which enables iterative development.

    4. Processing Speed and Compatibility

    Java is highly functional in several data science processes like data analysis, including data import, cleaning data, deep learning, statistical analysis, Natural Language Processing (NLP), and data visualization. The majority of code in Java is experimental. Java is a language that is statically typed and compiled, whereas Python is a dynamically organized and analyzed language. This single difference gives Java a faster runtime and more comfortable debugging.

    How to Get Started with Java in Data Science? 

    Getting started with Java in data science involves familiarizing yourself with the key libraries, tools, and frameworks that facilitate various aspects of the data science workflow. Below is a comprehensive guide to help you embark on your journey with Java in the field of data science. There is an amazing opportunity if you are interested to upskill yourself then swiftly join transformative Data Science course and get ahead in the field of technology. 

    Step1. Setup Your Java Development Environment 

    Install Java Development Kit (JDK): Download and install the latest version of the JDK from the official Oracle website or adopt OpenJDK. 

    Set up Integrated Development Environment (IDE): Choose an IDE for Java development. Popular choices include Eclipse, IntelliJ IDEA, and NetBeans. 

    Step 2. Learn the Basics of Java 

    Acquaint yourself with the basics of Java programming, including syntax, data types, control structures, and object-oriented programming principles. Numerous online tutorials and resources are available for Java beginners. 

    Step 3. Explore Data Manipulation with Java 

    Familiarize yourself with libraries that facilitate data manipulation in Java. The Apache Commons CSV library is useful for reading and writing CSV files, while the Apache POI library can handle Microsoft Excel files. For more advanced data manipulation tasks, consider using the Apache Commons Math library. 

    Step 4. Integrate Java with Databases 

    Learn how to connect Java applications to databases. JDBC (Java Database Connectivity) is a standard Java API for database access. Practice creating database connections, executing queries, and handling results. 

    Step 5. Understand Java for Big Data Processing 

    Explore Java's role in big data processing, particularly its integration with Apache Hadoop. Hadoop is a framework for distributed storage and processing of large datasets. Understand how to write MapReduce programs in Java for processing big data tasks. 

    Step 6. Dive into Machine Learning Libraries 

    Start working with machine learning libraries in Java. Apache Mahout provides a set of scalable machine learning algorithms, while Deeplearning4j is focused on deep learning. Experiment with basic algorithms for classification, regression, and clustering. 

    Step 7. Explore Apache Spark 

    Although primarily developed in Scala, Apache Spark has robust support for Java. Learn the basics of Spark and how to use its Java API for distributed data processing and machine learning tasks. This includes understanding Resilient Distributed Datasets (RDDs) and Spark's machine learning library (MLlib). 

    Step 8. Work with DL4J (Deep Learning for Java) 

    Dive into deep learning with DL4J, a deep learning library for Java. Explore its capabilities in building neural networks, training models, and making predictions. DL4J integrates well with Hadoop and Spark. 

    Step 9. Utilize Data Visualization Tools 

    Choose Java-based data visualization tools to enhance your ability to communicate insights. JFreeChart and JavaFX are popular choices for creating charts and interactive visualizations. 

    Step 10. Join the Java Data Science Community 

    Engage with the Java data science community through forums, blogs, and social media. Platforms like Stack Overflow and GitHub can provide valuable insights and solutions to common challenges. 

    Step 11. Stay Updated on Java and Data Science Trends 

    Keep abreast of the latest developments in Java and data science. Subscribe to relevant blogs, newsletters, and attend conferences to stay informed about emerging tools, techniques, and best practices. 

    Step 12. Build Real-World Projects 

    Apply your knowledge by working on real-world data science projects. This hands-on experience will solidify your skills and provide a portfolio to showcase to potential employers. 

    Where Does Java Fit In? 

    Java is being used in basically all the layers of web development. Web development at a very minimal level consists of a Client and a Server. Most of the famous and scalable frameworks for the Client, Server, and databases are built using Java. Java is very big in Financial Services. There are lots of global investment banks like Citigroup, Goldman Sachs, Barclays, Citigroup, Standard Charted, and other banks that use Java for reporting front and back headquarters electronic trading systems, reporting settlement and verification systems, data processing projects, and more. 

    Is Learning Java Mandatory? 

    If you wondering whether or not to learn a programming language, it depends on what you want to build with it. For example, if you like frontend development, learning C++ or an assembly language is not for you. Just like that, a game developer does not always pay a lot of attention to HTML and CSS. Each programming language is developed to serve a specific objective to start with.

    Java has evolved over the years and today the language finds its application in fields like fintech, eCommerce, custom enterprise web applications, android apps, distributed and data science. So, the bottom line is Java is not mandatory to learn but if you learn Java you will be able to develop your desired product. Here is how -

    • Lots of career opportunities: Java dominates in areas like the software industry, Enterprise services, web servers, Android apps. You get to choose the field of your choice as a Java developer.
    • Java follows Object Oriented Programming (OOP): By learning Java, you won’t just know the programming language, you will be equipped with one of the best methods in all programming today.  

    It is recommended to take part in a Data Science bootcamp and get a hands-on approach to building data science projects with Java.  


    Data Science is disrupting businesses along with other latest technologies. The challenges that businesses dealing with data science face are selecting the right stack of technologies, onboarding the right set of developers with the right set of Data Science skills. Java developers can make use of data science to produce virtually any product and it's particularly well-suited for building scalable platforms. 

    In case you realize that the tech stack you are using has restrictions, you can expand it by making something in Java. It's more comfortable for Java developers to use technologies that need grid computing. Java for data science is becoming common not only because Java is the "best" programming language for Data Science but java developers are known to come up with visions that many data science products and applications are built upon. The KnowledgeHut Data Science Bootcamp gives you the same opportunity where you work with more than 100 data samples and build data science projects around them. 

    Frequently Asked Questions (FAQs)

    1Is Java useful for data science?

    Java takes less time to execute a source code whereas Python is an interpreted language, which implies that the code is executed line by line. This results in slower performance of Python in terms of speed. Java is used in a number of processes involved in data science like data analysis, including , data import ,data cleaning. 

    2Why Java is not used in data science?

    Java is not the easiest programming language in this field of data science. It offers third-party open source libraries and any java developer can implement Machine Learning and get into data Science. However, beginners in the field of data science prefer languages like Python and R as they are relatively easier than Java.

    3Where is Java used in data science?

    Java could be used for many of the exact processes: data cleaning, data import, data export, statistical analysis. Most of the popular tools and frameworks used for Big Data are written in Java, including Hadoop ,Fink, Hadoop, Hive, Spark. Data Architects choose Java, because most of their frameworks are written in Java, and hence their APIs are more prepared for Java code than Python scripts. 


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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