Big Data Analytics with Java.
Learn the basics of analytics on big data using Java, machine learning and other big data toolsAbout This Book* 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 co...
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Full text (MCPHS users only) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2017
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
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| 100 | 1 | |a Mehta, Rajat. | |
| 245 | 1 | 0 | |a Big Data Analytics with Java. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (419 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a Advantages of naïve bayes. | ||
| 505 | 0 | |a Cover ; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Big Data Analytics with Java ; Why data analytics on big data?; Big data for analytics; Big data -- a bigger pay package for Java developers; Basics of Hadoop -- a Java sub-project; Distributed computing on Hadoop; HDFS concepts; Design and architecture of HDFS; Main components of HDFS; HDFS simple commands; Apache Spark; Concepts; Transformations; Actions; Spark Java API; Spark samples using Java 8; Loading data; Data operations -- cleansing and munging. | |
| 505 | 8 | |a Analyzing data -- count, projection, grouping, aggregation, and max/minActions on RDDs; Paired RDDs; Saving data; Collecting and printing results; Executing Spark programs on Hadoop; Apache Spark sub-projects; Spark machine learning modules; Mahout -- a popular Java ML library; Deeplearning4j -- a deep learning library; Summary; Chapter 2: First Steps in Data Analysis ; Datasets; Data cleaning and munging; Basic analysis of data with Spark SQL; Building SparkConf and context; Dataframe and datasets; Load and parse data; Analyzing data -- the Spark-SQL way. | |
| 505 | 8 | |a Spark SQL for data exploration and analyticsMarket basket analysis -- Apriori algorithm; Implementation of the Apriori algorithm in Apache Spark; Efficient market basket analysis using FP-Growth algorithm; Running FP-Growth on Apache Spark; Summary; Chapter 3: Data Visualization ; Data visualization with Java JFreeChart; Using charts in big data analytics; Time series chart; All India seasonal and annual average temperature series dataset; Simple single Time Series chart; Multiple Time Series on a single chart window; Bar charts; Histograms; When would you use a histogram? | |
| 505 | 8 | |a How to make histograms using JFreeChart?Line charts; Scatter plots; Box plots; Advanced visualization technique; Prefuse; IVTK Graph toolkit; Other libraries; Summary; Chapter 4: Basics of Machine Learning ; What is machine learning?; Real-life examples of machine learning; Type of machine learning; A small sample case study of supervised and unsupervised learning; Steps for machine learning problems; Choosing the machine learning model; What are the feature types that can be extracted from the datasets?; How do you select the best features to train your models? | |
| 505 | 8 | |a How do you run machine learning analytics on big data?Getting and preparing data in Hadoop; Training and storing models on big data; Apache Spark machine learning API; Summary; Chapter 5: Regression on Big Data ; Linear regression; What is simple linear regression?; Where is linear regression used?; Logistic regression; Which mathematical functions does logistic regression use?; Where is logistic regression used?; Predicting heart disease using logistic regression; Summary; Chapter 6: Naive Bayes and Sentiment Analysis ; Conditional probability; Bayes theorem; Naïve bayes algorithm. | |
| 520 | 8 | |a Learn the basics of analytics on big data using Java, machine learning and other big data toolsAbout This Book* 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. Who This Book Is ForThis book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. 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 datasetsIn DetailThis 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 Naive 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. Style and approachThe approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code. | |
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