Download or read book Neo4j Graph Data Science Certified written by Cristian Scutaru and published by . This book was released on 2021-04 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Who this book is for-Anyone interested in the new Neo4j Graph Data Science Certification exam.-Data Scientists trying to pass a FREE specialty exam.-Software Developers curious to learn advanced Graph Algorithms.-Neo4j Professionals looking to acquire new skills in graph databases.-All those looking for a higher score at the free online exam.-People with not enough time for long hands-on labs and courses.This book contains two original practice tests with 40 questions each, similar to the exam questions for the Neo4j Graph Data Science free online certification-Questions are similar and close to those found in the new online exam.-This is not a brain dump, but the very similar questions will help you understand the concepts behind.-In a separate section, you get explanations for each answer, with external references, and important hints.-The real exam is very similar to each practice test here: 40 total questions, in max 60 minutes, 80% passing score.-The exact same categories as in the online exam: Library (around 20%) ] Workflow (35%) + Algorithm (45%).-All Library questions are first, followed by Workflow questions, and ending up with Algorithm questions.Check also the interactive version of this book as an Udemy course, with the "Neo4j Graph Data Science Certified: Practice Exams" title.

Download or read book Graph Data Science with Neo4j written by Estelle Scifo and published by Packt Publishing Ltd. This book was released on 2023-01-31 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge your data with the limitless potential of Neo4j 5, the premier graph database for cutting-edge machine learning Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesExtract meaningful information from graph data with Neo4j's latest version 5Use Graph Algorithms into a regular Machine Learning pipeline in PythonLearn the core principles of the Graph Data Science Library to make predictions and create data science pipelines.Book Description Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You'll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you'll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you'll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you'll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions. What you will learnUse the Cypher query language to query graph databases such as Neo4jBuild graph datasets from your own data and public knowledge graphsMake graph-specific predictions such as link predictionExplore the latest version of Neo4j to build a graph data science pipelineRun a scikit-learn prediction algorithm with graph dataTrain a predictive embedding algorithm in GDS and manage the model storeWho this book is for If you're a data scientist or data professional with a foundation in the basics of Neo4j and are now ready to understand how to build advanced analytics solutions, you'll find this graph data science book useful. Familiarity with the major components of a data science project in Python and Neo4j is necessary to follow the concepts covered in this book.

Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Download or read book Hands On Graph Analytics with Neo4j written by Estelle Scifo and published by Packt Publishing Ltd. This book was released on 2020-08-21 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.

Download or read book Graph Algorithms for Data Science written by Tomaž Bratanic and published by Simon and Schuster. This book was released on 2024-02-27 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

Download or read book Neo4j Certified Professional Exam Practice Tests written by Cristian Scutaru and published by Cristian Scutaru. This book was released on 2020-12-29 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains two high-quality practice tests of 80 questions each - with answers and explanations - to help you pass or improve your score on the free online Neo4j Certified Professional exam. * All questions are closely emulated from those currently found in the actual exam, so you'll not waste time on anything else. * Unlike the real exam, you'll know right away what questions you missed, and what the correct answers are. * Detailed explanations with external references for any possible choice, in each practice test question. * Quiz types distributed close to 50% multi-choice + 25% multi-select + 25% True/False. * Domains distributed close to the real exam: 40% Cypher + 30% Intro + 20% Modeling + 10% Developer. * Questions will test you on Neo4j version 3.x, but explanations will have updates on deprecated features and change history. Why not just trying again and again the free online exam, until I pass? * Because starting May 2020, you can try only once a day the real online exam. * Because the high number of multi-answer questions and the gotcha tricks may give you no idea what went wrong. * Because it is time consuming and you can easily get stuck at the same low scoring mark. * Because nothing tells you where and what you failed, and next time you will likely make the same wrong choices. * Because you can hardly improve without knowing what went wrong. * Because you may want to get a better passing score anyway, as long as it appears on your issued certificate. Same e-book as LIVE practice tests on Udemy: "Neo4j Certified Professional - Practice Exams".

Download or read book Graph Data Science with Python and Neo4j written by Timothy Eastridge and published by Orange Education Pvt Ltd. This book was released on 2024-03-11 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical approaches to leveraging graph data science to solve real-world challenges. KEY FEATURES ● Explore the fundamentals of graph data science, its importance, and applications. ● Learn how to set up Python and Neo4j environments for graph data analysis. ● Discover techniques to visualize complex graph networks for better understanding. DESCRIPTION Graph Data Science with Python and Neo4j is your ultimate guide to unleashing the potential of graph data science by blending Python's robust capabilities with Neo4j's innovative graph database technology. From fundamental concepts to advanced analytics and machine learning techniques, you'll learn how to leverage interconnected data to drive actionable insights. Beyond theory, this book focuses on practical application, providing you with the hands-on skills needed to tackle real-world challenges. You'll explore cutting-edge integrations with Large Language Models (LLMs) like ChatGPT to build advanced recommendation systems. With intuitive frameworks and interconnected data strategies, you'll elevate your analytical prowess. This book offers a straightforward approach to mastering graph data science. With detailed explanations, real-world examples, and a dedicated GitHub repository filled with code examples, this book is an indispensable resource for anyone seeking to enhance their data practices with graph technology. Join us on this transformative journey across various industries, and unlock new, actionable insights from your data. WHAT WILL YOU LEARN ● Set up and utilize Python and Neo4j environments effectively for graph analysis. ● Import and manipulate data within the Neo4j graph database using Cypher Query Language. ● Visualize complex graph networks to gain insights into data relationships and patterns. ● Enhance data analysis by integrating ChatGPT for context-rich data enrichment. ● Explore advanced topics including Neo4j vector indexing and Retrieval-Augmented Generation (RAG). ● Develop recommendation engines leveraging graph embeddings for personalized suggestions. ● Build and deploy recommendation systems and fraud detection models using graph techniques. ● Gain insights into the future trends and advancements shaping the field of graph data science. WHO IS THIS BOOK FOR? This book caters to a diverse audience interested in leveraging the power of graph data science using Python and Neo4j. It includes Data Science Professionals, Software Engineers, Academic Researchers, Business Analysts, and Technology Hobbyists. This comprehensive book equips readers from various backgrounds to effectively utilize graph data science in their respective fields. TABLE OF CONTENTS 1. Introduction to Graph Data Science 2. Getting Started with Python and Neo4j 3. Import Data into the Neo4j Graph Database 4. Cypher Query Language 5. Visualizing Graph Networks 6. Enriching Neo4j Data with ChatGPT 7. Neo4j Vector Index and Retrieval-Augmented Generation (RAG) 8. Graph Algorithms in Neo4j 9. Recommendation Engines Using Embeddings 10. Fraud Detection CLOSING SUMMARY The Future of Graph Data Science Index

Download or read book Learning Neo4j written by Rik Van Bruggen and published by Packt Publishing Ltd. This book was released on 2014-08-25 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily.

Download or read book Mastering Neo4j database written by Cybellium Ltd and published by Cybellium Ltd. This book was released on with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Full Potential of Graph Databases with "Mastering Neo4j Database" In today's data-driven world, the ability to make sense of complex relationships within vast datasets is paramount. "Mastering Neo4j Database" is your comprehensive guide to mastering one of the most powerful graph databases available – Neo4j. Whether you're a seasoned data professional or a newcomer to the realm of graph technology, this book equips you with the knowledge and skills needed to harness the true capabilities of Neo4j. About the Book: "Mastering Neo4j Database" takes you on a journey through the intricacies of graph databases and Neo4j. From the foundational concepts of graph theory to advanced techniques in data modeling, querying, and optimization, this book covers it all. Each chapter is carefully crafted to provide both a deep understanding of the concepts and practical applications in real-world scenarios. Key Features: · Graph Database Fundamentals: Lay a solid foundation by exploring the core concepts of graph databases, graph theory, and how Neo4j revolutionizes data representation. · Data Modeling: Learn how to design effective graph data models that accurately reflect your domain's relationships, ensuring efficient data storage and retrieval. · Cypher Query Language: Master the powerful Cypher query language used in Neo4j to perform complex graph traversals, pattern matching, and data manipulation. · Performance Optimization: Discover strategies for optimizing query performance and database design, ensuring Neo4j scales seamlessly as your data grows. · Advanced Techniques: Dive into advanced topics such as graph algorithms, full-text search, and integrating Neo4j with other tools and frameworks. · Real-World Applications: Explore a range of real-world use cases across industries, from social networks and recommendation systems to fraud detection and knowledge graphs. · Graph Data Science: Learn how to leverage Neo4j's graph data science capabilities to gain insights, perform machine learning, and uncover hidden patterns. · Administration and Security: Understand the best practices for managing and securing your Neo4j databases, ensuring data integrity and user privacy. Who This Book Is For: "Mastering Neo4j Database" is designed for data architects, developers, database administrators, and anyone passionate about leveraging the power of graph databases. Whether you're new to the world of graphs or looking to deepen your expertise, this book provides the insights and tools to navigate the complexities of Neo4j. Why You Should Read This Book: In an age where connected data is the foundation of innovation, Neo4j stands as a leader in graph database technology. "Mastering Neo4j Database" empowers you to fully unlock its potential, enabling you to create more insightful applications, perform advanced analytics, and make data-driven decisions that were once out of reach. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Download or read book Neo4j High Performance written by Sonal Raj and published by Packt Publishing Ltd. This book was released on 2015-03-02 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are a professional or enthusiast who has a basic understanding of graphs or has basic knowledge of Neo4j operations, this is the book for you. Although it is targeted at an advanced user base, this book can be used by beginners as it touches upon the basics. So, if you are passionate about taming complex data with the help of graphs and building high performance applications, you will be able to get valuable insights from this book.

Download or read book Neo4j Graph Data Modeling written by Mahesh Lal and published by Packt Publishing Ltd. This book was released on 2015-07-27 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neo4j is a graph database that allows you to model your data as a graph and find solutions to complex real-world problems that are difficult to solve using any other type of database. This book is designed to help you understand the intricacies of modeling a graph for any domain. The book starts with an example of a graph problem and then introduces you to modeling non-graph problems using Neo4j. Concepts such as the evolution of your database, chains, access control, and recommendations are addressed, along with examples and are modeled in a graph. Throughout the book, you will discover design choices and trade-offs, and understand how and when to use them. By the end of the book, you will be able to effectively use Neo4j to model your database for efficiency and flexibility.

Download or read book Graph Databases written by Ian Robinson and published by "O'Reilly Media, Inc.". This book was released on 2013-06-10 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information

Download or read book Neo4j in Action written by Tareq Abedrabbo and published by Simon and Schuster. This book was released on 2014-12-05 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Neo4j in Action is a comprehensive guide to Neo4j, aimed at application developers and software architects. Using hands-on examples, you'll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Much of the data today is highly connected—from social networks to supply chains to software dependency management—and more connections are continually being uncovered. Neo4j is an ideal graph database tool for highly connected data. It is mature, production-ready, and unique in enabling developers to simply and efficiently model and query connected data. About the Book Neo4j in Action is a comprehensive guide to designing, implementing, and querying graph data using Neo4j. Using hands-on examples, you'll learn to model graph domains naturally with Neo4j graph structures. The book explores the full power of native Java APIs for graph data manipulation and querying. It also covers Cypher, Neo4j's graph query language. Along the way, you'll learn how to integrate Neo4j into your domain-driven app using Spring Data Neo4j, as well as how to use Neo4j in standalone server or embedded modes. Knowledge of Java basics is required. No prior experience with graph data or Neo4j is assumed. What's Inside Graph database patterns How to model data in social networks How to use Neo4j in your Java applications How to configure and set up Neo4j About the Authors Aleksa Vukotic is an architect specializing in graph data models. Nicki Watt, Dominic Fox, Tareq Abedrabbo, and Jonas Partner work at OpenCredo, a Neo Technology partner, and have been involved in many projects using Neo4j. Table of Contents PART 1 INTRODUCTION TO NEO4J A case for a Neo4j database Data modeling in Neo4j Starting development with Neo4j The power of traversals Indexing the data PART 2 APPLICATION DEVELOPMENT WITH NEO4J Cypher: Neo4j query language Transactions Traversals in depth Spring Data Neo4j PART 3 NEO4J IN PRODUCTION Neo4j: embedded versus server mode

Download or read book Graph Powered Machine Learning written by Alessandro Negro and published by Simon and Schuster. This book was released on 2021-10-05 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs

Download or read book Introducing Data Science written by Davy Cielen and published by Simon and Schuster. This book was released on 2016-05-02 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user

Download or read book Building Knowledge Graphs written by Jesus Barrasa and published by "O'Reilly Media, Inc.". This book was released on 2023-06-22 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by storing interlinked descriptions of entities—objects, events, situations, or abstract concepts—and encoding the underlying information. How do you create a knowledge graph? And how do you move it from theory into production? Using hands-on examples, this practical book shows data scientists and data engineers how to build their own knowledge graphs. Authors Jesús Barrasa and Jim Webber from Neo4j illustrate common patterns for building knowledge graphs that solve many of today’s pressing knowledge management problems. You’ll quickly discover how these graphs become increasingly useful as you add data and augment them with algorithms and machine learning. Learn the organizing principles necessary to build a knowledge graph Explore how graph databases serve as a foundation for knowledge graphs Understand how to import structured and unstructured data into your graph Follow examples to build integration-and-search knowledge graphs Learn what pattern detection knowledge graphs help you accomplish Explore dependency knowledge graphs through examples Use examples of natural language knowledge graphs and chatbots Use graph algorithms and ML to gain insight into connected data

Download or read book Big Data Analytics with Java written by Rajat Mehta and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the basics of analytics on big data using Java, machine learning and other big data tools About 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 For This 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 datasets In Detail 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 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 approach The 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.