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Java: Data Science Made Easy

You're reading from Java: Data Science Made Easy Data collection, processing, analysis, and more

Product type Course

Published in Jul 2017

Publisher Packt

ISBN-13 9781788475655

Length 734 pages

Edition 1st Edition

Languages

Java

Tools

Maven

Concepts

Data Science

Authors (3):

Alexey Grigorev

Richard M. Reese

Jennifer L. Reese

View More author details

Table of Contents (29) Chapters

Title Page

Credits

Preface

1. Module 1

2. Getting Started with Data Science FREE CHAPTER

3. Data Acquisition

4. Data Cleaning

5. Data Visualization

6. Statistical Data Analysis Techniques

7. Machine Learning

8. Neural Networks

9. Deep Learning

10. Text Analysis

11. Visual and Audio Analysis

12. Visual and Audio Analysis

13. Mathematical and Parallel Techniques for Data Analysis

14. Bringing It All Together

15. Module 2

16. Data Science Using Java

17. Data Processing Toolbox

18. Exploratory Data Analysis

19. Supervised Learning - Classification and Regression

20. Unsupervised Learning - Clustering and Dimensionality Reduction

21. Working with Text - Natural Language Processing and Information Retrieval

22. Extreme Gradient Boosting

23. Deep Learning with DeepLearning4J

24. Scaling Data Science

25. Deploying Data Science Models

26. Bibliography

Chapter 18. Exploratory Data Analysis

the previous chapter,Â we covered data processing, which is an important step for transforming data into a form usable for analysis. In this chapter, we take the next logical step after cleaning and look at data. This step is called Exploratory Data Analysis (EDA), and it consists of summarizing data and creating visualizations.

In this chapter, we will cover the following topics:

Summary statistics with Apache Commons Math and Joinery
Interactive shells for EDA in Java and JVM

By the end of this chapter, you will know how to calculate summary statistics and create simple graphs with Joinery.Â

The rest of the chapter is locked

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Authors (3)

Alexey Grigorev

Alexey Grigorev

Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 8 years of professional experience. He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Simplaex, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling. His areas of expertise are machine learning and text mining.

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Jennifer L. Reese

Jennifer L. Reese

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.

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Richard M. Reese

Richard M. Reese

Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java

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