Data science in Java

In this book, we will use Java for doing data science projects. Java might not seem a good choice for data science at first glance, unlike Python or R, it has fewer data science and machine learning libraries, it is more verbose and lacks interactivity. On the other hand, it has a lot of upsides as follows:

• Java is a statically typed language, which makes it easier to maintain the code base and harder to make silly mistakes--the compiler can detect some of them.
• The standard library for data processing is very rich, and there are even richer external libraries.
• Java code is typically faster than the code in scripting languages that are usually used for data science (such as R or Python).
• Maven, the de-facto standard for dependency management in the Java world, makes it very easy to add new libraries to the project and avoid version conflicts.
• Most of big data frameworks for scalable data processing are written in either Java or JVM languages, such as Apache Hadoop, Apache Spark, or Apache Flink.
• Very often production systems are written in Java and building models in other languages adds unnecessary levels of complexity. Creating the models in Java makes it easier to integrate them to the product.

Next, we will look at the data science libraries available in Java.