- Acquire real-world set of tools for building enterprise level data science applications
- Surpasses the barrier of other languages in data science and learn create useful object-oriented codes
- Extensive use of Java compliant big data tools like apache spark, Hadoop, etc.
This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset.
This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
What you will learn
- Start from simple analytic tasks on big data
- Get into more complex tasks with predictive analytics on big data using machine learning
- Learn real time analytic tasks
- Understand the concepts with examples and case studies
- Prepare and refine data for analysis
- Create charts in order to understand the data
- See various real-world datasets
About the Author
The author is a VP (Technical Architect) in technology in JP Morgan Chase in New York. The author is a sun certified java developer and has worked on java related technologies for more than 16 years. Current role for the past few years heavily involves the usage of bid data stack and running analytics on it. Author is also a contributor in various open source projects that are available on his GitHub repository and is also a frequent write on dev magazines.
Table of Contents
Chapter 1. Big Data Analytics with Java
Chapter 2. First Steps in Data Analysis
Chapter 3. Data Visualization
Chapter 4. Basics of Machine Learning
Chapter 5. Regression on Big Data
Chapter 6. Naive Bayes and Sentiment Analysis
Chapter 7. Decision Trees
Chapter 8. Ensembling on Big Data
Chapter 9. Recommendation Systems
Chapter 10. Clustering and Customer Segmentation on Big Data
Chapter 11. Massive Graphs on Big Data
Chapter 12. Real-Time Analytics on Big Data
Chapter 13. Deep Learning Using Big Data