Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

Arrow left icon
Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

The use of statistical methods in data science

Statistical analysis is the key to many data science tasks. It is used for many types of analysis ranging from the computation of simple mean and medium to complex multiple regression analysis. Chapter 5, Statistical Data Analysis Techniques, introduces this type of analysis and the Java support available.

Statistical analysis is not always an easy task. In addition, advanced statistical techniques often require a particular mindset to fully comprehend, which can be difficult to learn. Fortunately, many techniques are not that difficult to use and various libraries mitigate some of these techniques' inherent complexity.

Regression analysis, in particular, is an important technique for analyzing data. The technique attempts to draw a line that matches a set of data. An equation representing the line is calculated and can be used to predict future behavior. There are several types of regression analysis, including simple and multiple regression. They vary by the number of variables being considered.

The following graph shows the straight line that closely matches a set of data points representing the population of Belgium over several decades:

The use of statistical methods in data science

Simple statistical techniques, such as mean and standard deviation, can be computed using basic Java. They can also be handled by libraries such as Apache Commons. For example, to calculate the median, we can use the Apache Commons DescriptiveStatistics class. This is illustrated next where the median of an array of doubles is calculated. The numbers are added to an instance of this class, as shown here:

double[] testData = {12.5, 18.3, 11.2, 19.0, 22.1, 14.3, 16.2, 
    12.5, 17.8, 16.5, 12.5}; 
DescriptiveStatistics statTest =  
    new SynchronizedDescriptiveStatistics(); 
for(double num : testData){ 
   statTest.addValue(num); 
} 

The getPercentile method returns the value stored at the percentile specified in its argument. To find the median, we use the value of 50.

out.println("The median is " + statTest.getPercentile(50)); 
 

Our output is as follows:

The median is 16.2

In Chapter 5, Statistical Data Analysis Techniques, we will demonstrate how to perform regression analysis using the Apache Commons SimpleRegression class.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image