Groovy is a powerful multi-paradigm programming language for the JVM that offers a wealth of features that make it ideal for many data science and big data scenarios.
* Groovy has a dynamic nature like Python, which means that it is very powerful, easy to learn, and productive. The language gets out of the way and lets data scientists write their algorithms naturally.
* Groovy has a static nature like Java and Kotlin, which makes it fast when needed. Its close alignment with Java means that you can often just cut-and-paste the Java examples from various big data solutions and they'll work just fine in Groovy.
* Groovy has first-class functional support, meaning that it offers features and allows solutions similar to Scala. Functional and stream processing with immutable data structures can offer many advantages when working in parallel processing or clustered environments.
These slides review the key benefits of using Groovy to develop data science solutions, including integration with various JDK libraries commonly used in data science solutions including libraries for data manipulation, machine learning, plotting and various big data solutions for scaling up these algorithms.
Topics covered include: regression, clustering, classification, neural networks, language processing, image detection.
Math/Data Science libraries covered include:
Weka, Smile, Tablesaw, Apache Commons Math, Jupyter/Beakerx notebooks, Deep Learning4J.
Libraries for scaling/concurrency include:
GPars, Apache Spark, Apache Ignite, Apache MXNet, Apache Beam, Apache NLPCraft.