Top 5 Java Libraries to solve your Data Science Problems

Data Science and Artificial Intelligence have been some of the most talked-about and discussed technologies in recent years. The evolutions in the field of technology have taken business and automation to an elevated level. Many organizations are investing millions of dollars in research and people to build and develop these incredibly powerful data-driven applications.


When we talk about Data Science and Artificial Intelligence, the only programming languages considered for implementation are Python and R. However, the news here is that Java can also serve the same purpose. From enterprise-level business solutions and navigation systems to mobile phones and applications, Java applies to nearly every area of technology with the help of powerful third-party open source libraries. In fact, Java is considered as one of the top programming languages in the field of Data Science.

Interesting! Isn’t it? Let’s have a glance at the libraries of this underdog programming language.


Top 5 Java Libraries for Data Science

Deeplearning4j – DL4J

DL4J or Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep learning library for Java and Scala. It is integrated with the latest distributed computing frameworks such as Apache Spark, Hadoop, and Spark providing AI to business using GPUs and CPUs.

Some of its features are:

  • Detailed API doc
  • Brings AI to business environments
  • Sample projects in multiple languages



Weka stands for Waikato Environment for Knowledge Analysis. It is a free, open-source machine learning library for Java. Weka is an assembly of ML algorithms and it also maintains deep learning. It is ideal for beginners who want to understand the know-how of machine learning as they can simply do so without writing a line of code. It majorly focuses on:

  • Data Mining
  • Classification
  • Regression
  • Clustering


Click here to know how to use Weka in your Java code.



ADAMS stands for Advanced Data Mining and Machine Learning System. It provides flexibility for building and maintaining a data-driven, reactive workflow that can be easily integrated.  ADAMS employs a tree-like structure and follows a philosophy of less is “more”. It is released under the GPLv3 license. Some of its features are:

  • Machine Learning
  • Streaming
  • Data Processing
  • Documentation



Java-ML is a compilation of machine learning and data mining algorithms that are primarily penned in Java. These algorithms have a well-documented source code that is accessible from the API documentation.  It only provides a standard interface for algorithms. Some of its features are:

  • Data Manipulation
  • Clustering
  • Classification
  • Feature Selection
  • Documentation



Apache Mahout is a distributed framework that provides implementations of machine algorithms for the Apache Hadoop platform. This ML framework aids in working with built-in algorithms. With the scalable ML libraries, a customized recommendation system can be constructed using a rich set of components.

It majorly focuses on:

  • Recommendation systems
  • Clustering
  • Classifications


So that enhances our know-how on the additional features of Java libraries and how we can avail the various java tools for carrying out data science operations.

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