Getting Started With Data Analysis in Java: Statistical Features

Data processing and analysis in Java — or increasingly in web environments with Spring Boot (a popular Java framework)— is a common approach. For instance, you can run an initial statistical analysis to get valuable insight into given data or perform the pre-processing or feature extraction of the data for machine learning (ML) use cases. In addition, many well-known frameworks in the areas of data science, data processing (e.g., <a href="https://spark.apache.org/" rel="noopener ugc nofollow" target="_blank">Apache Spark</a>), data analysis, data visualization, NLP (e.g., <a href="https://nlp.stanford.edu/software/" rel="noopener ugc nofollow" target="_blank">Stanford CoreNLP</a>), or ML (e.g., <a href="https://moa.cms.waikato.ac.nz/" rel="noopener ugc nofollow" target="_blank">MOA</a>, <a href="https://www.cs.waikato.ac.nz/ml/weka/" rel="noopener ugc nofollow" target="_blank">WEKA</a>) are for or at least compatible with Java. <a href="https://medium.com/javarevisited/getting-started-with-data-analysis-in-java-statistical-features-2e091ff10424">Click Here</a>