Get the most out of the popular Java libraries and tools to perform efficient data analysis
About This Book
- Get your basics right for data analysis with Java and make sense of your data through effective visualizations.
- Use various Java APIs and tools such as Rapidminer and WEKA for effective data analysis and machine learning.
- This is your companion to understanding and implementing a solid data analysis solution using Java
Who This Book Is For
If you are a student or Java developer or a budding data scientist who wishes to learn the fundamentals of data analysis and learn to perform data analysis with Java, this book is for you. Some familiarity with elementary statistics and relational databases will be helpful but is not mandatory, to get the most out of this book. A firm understanding of Java is required.
What You Will Learn
- Develop Java programs that analyze data sets of nearly any size, including text
- Implement important machine learning algorithms such as regression, classification, and clustering
- Interface with and apply standard open source Java libraries and APIs to analyze and visualize data
- Process data from both relational and non-relational databases and from time-series data
- Employ Java tools to visualize data in various forms
- Understand multimedia data analysis algorithms and implement them in Java.
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks.
This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you'll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression.
In the process, you'll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs.
By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Style and approach
The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy-to-follow examples, this book will turn you into an ace data analyst in no time.
Table of contents
Java Data Analysis
- Table of Contents
- Java Data Analysis
- About the Author
- About the Reviewers
- Customer Feedback
- 1. Introduction to Data Analysis
- 2. Data Preprocessing
- 3. Data Visualization
- Descriptive statistics
- Random sampling
- Random variables
- Probability distributions
- Cumulative distributions
- The binomial distribution
- Multivariate distributions
- Conditional probability
- The independence of probabilistic events
- Contingency tables
- Bayes' theorem
- Covariance and correlation
- The standard normal distribution
- The central limit theorem
- Confidence intervals
- Hypothesis testing
- 5. Relational Databases
- 6. Regression Analysis
7. Classification Analysis
- Decision trees
- Bayesian classifiers
- Logistic regression
- 8. Cluster Analysis
- 9. Recommender Systems
- 10. NoSQL Databases
- 11. Big Data Analysis with Java
- A. Java Tools
- Title: Java Data Analysis
- Release date: September 2017
- Publisher(s): Packt Publishing
- ISBN: 9781787285651
You might also like
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
40 Algorithms Every Programmer Should Know
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental …
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …
Java: Data Science Made Easy
Data collection, processing, analysis, and more About This Book Your entry ticket to the world of …