Book Image

Java Data Science Cookbook

By : Rushdi Shams
Book Image

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
About the Author
About the Reviewer
Customer Feedback


Data visualization is becoming increasingly popular in the data science community because it is the visual communication of information using the underlying data with the help of dots, lines, or bars. Visualization not only communicates information to the data scientist, but also presents it to an audience with no or little knowledge of the underlying data distribution or the nature of the data. On many occasions, data visualization is used by management, stakeholders, and business executives to make decisions or to understand trends.

In this chapter, we present seven recipes to visualize data using sine graphs, histograms, bar charts, box plots, scatter plots, donut or pie plots, and area graphs. This being a cookbook, we do not give enough background on these plots, their advantages, and the area of usage except a very short introduction on them. Rather, we focus on the technicalities of the Java library that can accomplish the visualizations.

In this chapter, we will be using...