Java Data Science Made Easy

Java  Data Science Made Easy
Author: Richard M. Reese,Jennifer L. Reese,Alexey Grigorev
Publsiher: Packt Publishing Ltd
Total Pages: 715
Release: 2017-07-07
Genre: Computers
ISBN: 9781788479189

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Data collection, processing, analysis, and more About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples A highly practical course covering a broad set of topics - from the basics of Machine Learning to Deep Learning and Big Data frameworks. Who This Book Is For This course is meant for Java developers who are comfortable developing applications in Java, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Java programming language will also find this book to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing Java stack, this book is for you! What You Will Learn Understand the key concepts of data science Explore the data science ecosystem available in Java Work with the Java APIs and techniques used to perform efficient data analysis Find out how to approach different machine learning problems with Java Process unstructured information such as natural language text or images, and create your own search Learn how to build deep neural networks with DeepLearning4j Build data science applications that scale and process large amounts of data Deploy data science models to production and evaluate their performance In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this course, we cover the basic as well as advanced data science concepts and how they are implemented using the popular Java tools and libraries.The course starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. You will examine the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. Throughout this course, the chapters will illustrate a challenging data science problem, and then go on to present a comprehensive, Java-based solution to tackle that problem. You will cover a wide range of topics – from classification and regression, to dimensionality reduction and clustering, deep learning and working with Big Data. Finally, you will see the different ways to deploy the model and evaluate it in production settings. By the end of this course, you will be up and running with various facets of data science using Java, in no time at all. This course contains premium content from two of our recently published popular titles: Java for Data Science Mastering Java for Data Science Style and approach This course follows a tutorial approach, providing examples of each of the concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Java for Data Science

Java for Data Science
Author: Richard M. Reese,Jennifer L. Reese
Publsiher: Packt Publishing Ltd
Total Pages: 386
Release: 2017-01-10
Genre: Computers
ISBN: 9781785281242

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Examine the techniques and Java tools supporting the growing field of data science About This Book Your entry ticket to the world of data science with the stability and power of Java Explore, analyse, and visualize your data effectively using easy-to-follow examples Make your Java applications more capable using machine learning Who This Book Is For This book is for Java developers who are comfortable developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful. What You Will Learn Understand the nature and key concepts used in the field of data science Grasp how data is collected, cleaned, and processed Become comfortable with key data analysis techniques See specialized analysis techniques centered on machine learning Master the effective visualization of your data Work with the Java APIs and techniques used to perform data analysis In Detail Data science is concerned with extracting knowledge and insights from a wide variety of data sources to analyse patterns or predict future behaviour. It draws from a wide array of disciplines including statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques, to provide you with an understanding of their purpose and application. The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data, followed by a discussion of resources that support parallel implementation. The final chapter illustrates an in-depth data science problem and provides a comprehensive, Java-based solution. Due to the nature of the topic, simple examples of techniques are presented early followed by a more detailed treatment later in the book. This permits a more natural introduction to the techniques and concepts presented in the book. Style and approach This book follows a tutorial approach, providing examples of each of the major concepts covered. With a step-by-step instructional style, this book covers various facets of data science and will get you up and running quickly.

Mastering Java for Data Science

Mastering Java for Data Science
Author: Alexey Grigorev
Publsiher: Unknown
Total Pages: 393
Release: 2017-02-28
Genre: Electronic Book
ISBN: 1782174273

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Become an expert at building and deploying enterprise-grade data applications in JavaAbout This Book* This comprehensive book shows you exactly how you can take your Java data science applications to production seamlessly* Dive deep into analytics, supervised and unsupervised learning, and much more with ease* Explore Java's various libraries to efficiently build and deploy data applications for the enterpriseWho This Book Is ForThis book is for those Java developers who are comfortable with developing applications in Java and are familiar with the basic concepts of data science. This is the go-to book for anyone looking to master the subject using Java. If you are willing to build efficient data applications in your enterprise environment without changing your existing stack, this book is for you!What you will learn* Get a solid understanding of the data processing toolbox available in Java* Explore the data science ecosystem available in Java and other JVM languages* Understand when to use Java and what is best to do outside of Java* Deal with the machine learning task at hand and bring the results directly to production* Get state-of-the-art performance with xgboost and deeplearning4j* Build applications that scale and process large amounts of data in real timeIn DetailJava is the language of choice if you want to bring data science to production, thanks to its stability and rich set of libraries. Major big data solutions including Hadoop are written in Java. This book will teach you how to perform data analysis on big data in a much more sophisticated manner. If you are willing to take your data products to enterprise without changing your stack, this book will tell you how to do it with ease.This book will quickly brush up on what you already know about using Java in data science applications and will then dive quickly into the advanced concepts to implement data science in production. The book covers topics such as advanced data science algorithms, preparing tricky data, advanced clustering, regression, classification, prediction, machine learning, and more.We'll teach you how data science can be used effectively to analyze unstructured data and big data. This book will enable you to tackle the problems of advanced visualization, advanced statistics, scaling data science applications, deploying these applications in production, and many more. You will also learn about natural language processing, real-time analytics, deep learning, and neural networks.

Artificial Intelligence

Artificial Intelligence
Author: Code Well Academy
Publsiher: Createspace Independent Publishing Platform
Total Pages: 150
Release: 2016-04-10
Genre: Electronic Book
ISBN: 1530826861

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Design the MIND of a Robotic Thinker! " Every chapter is very clearly described and all of the information was presented consistently. " - Amazon Customer " Within this book you'll find GREAT coding skills to learn. Here I've learned so much from reading this book. " - Stella Mill, from Amazon.com " This is the most complete and comprehensive book I read on a subject of Artificial Intelligence so far and it's very well written as well. " - Falli Conna, from Amazon.com * * INCLUDED BONUS: a Quick-start guide to Learning Ruby in less than a Day! * * How would you like to Create the Next AI bot? Artificial Intelligence. One of the most brilliant creations of mankind. No longer a sci-fi fantasy, but a realistic approach to making work more efficient and lives easier.And the best news? It's not that complicated after all Does it require THAT much advanced math? NO!And are you paying THOUSANDS of dollars just to learn this information? NO!Hundreds? Not even close. Within this book's pages, you'll find GREAT coding skills to learn - and more. Just some of the questions and topics include: - Complicated scheduling problem? Here's how to solve it. - How good are your AI algorithms? Analysis for Efficiency- How to interpret a system into logical code for the AI- How would an AI system would diagnose a system? We show you...- Getting an AI agent to solve problems for youand Much, much more!World-Class TrainingThis book breaks your training down into easy-to-understand modules. It starts from the very essentials of algorithms and program procedures, so you can write great code - even as a beginner!

Big Data Analytics with Java

Big Data Analytics with Java
Author: Rajat Mehta
Publsiher: Packt Publishing Ltd
Total Pages: 418
Release: 2017-07-31
Genre: Computers
ISBN: 9781787282193

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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.

Java Data Analysis

Java Data Analysis
Author: John R. Hubbard
Publsiher: Packt Publishing Ltd
Total Pages: 412
Release: 2017-09-19
Genre: Computers
ISBN: 9781787286405

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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. In Detail 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.

Data Structures and Algorithms in Java

Data Structures and Algorithms in Java
Author: Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
Publsiher: John Wiley & Sons
Total Pages: 736
Release: 2014-01-28
Genre: Computers
ISBN: 9781118771334

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The design and analysis of efficient data structures has long been recognized as a key component of the Computer Science curriculum. Goodrich, Tomassia and Goldwasser's approach to this classic topic is based on the object-oriented paradigm as the framework of choice for the design of data structures. For each ADT presented in the text, the authors provide an associated Java interface. Concrete data structures realizing the ADTs are provided as Java classes implementing the interfaces. The Java code implementing fundamental data structures in this book is organized in a single Java package, net.datastructures. This package forms a coherent library of data structures and algorithms in Java specifically designed for educational purposes in a way that is complimentary with the Java Collections Framework.

JavaScript for Data Science

JavaScript for Data Science
Author: Maya Gans,Toby Hodges,Greg Wilson
Publsiher: CRC Press
Total Pages: 232
Release: 2020-02-03
Genre: Computers
ISBN: 9781000028591

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JavaScript is the native language of the Internet. Originally created to make web pages more dynamic, it is now used for software projects of all kinds, including scientific visualization and data services. However, most data scientists have little or no experience with JavaScript, and most introductions to the language are written for people who want to build shopping carts rather than share maps of coral reefs. This book will introduce you to JavaScript's power and idiosyncrasies and guide you through the key features of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser. Topics include: The core features of modern JavaScript Creating templated web pages Making those pages interactive using React Data visualization using Vega-Lite Using Data-Forge to wrangle tabular data Building a data service with Express Unit testing with Mocha All of the material is covered by the Creative Commons Attribution-Noncommercial 4.0 International license (CC-BY-NC-4.0) and is included in the book's companion website at http://js4ds.org . Maya Gans is a freelance data scientist and front-end developer by way of quantitative biology. Toby Hodges is a bioinformatician turned community coordinator who works at the European Molecular Biology Laboratory. Greg Wilson co-founded Software Carpentry, and is now part of the education team at RStudio

Scalability Patterns

Scalability Patterns
Author: Chander Dhall
Publsiher: Apress
Total Pages: 158
Release: 2018-07-20
Genre: Computers
ISBN: 9781484210734

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In this book, the CEO of Cazton, Inc. and internationally-acclaimed speaker, Chander Dhall, demonstrates current website design scalability patterns and takes a pragmatic approach to explaining their pros and cons to show you how to select the appropriate pattern for your site. He then tests the patterns by deliberately forcing them to fail and exposing potential flaws before discussing how to design the optimal pattern to match your scale requirements. The author explains the use of polyglot programming and how to match the right patterns to your business needs. He also details several No-SQL patterns and explains the fundamentals of different paradigms of No-SQL by showing complementary strategies of using them along with relational databases to achieve the best results. He also teaches how to make the scalability pattern work with a real-world microservices pattern. With the proliferation of countless electronic devices and the ever growing number of Internet users, the scalability of websites has become an increasingly important challenge. Scalability, even though highly coveted, may not be so easy to achieve. Think that you can't attain responsiveness along with scalability? Chander Dhall will demonstrate that, in fact, they go hand in hand. What You'll Learn Architect and develop applications so that they are easy to scale. Learn different scaling and partitioning options and the combinations. Learn techniques to speed up responsiveness. Deep dive into caching, column-family databases, document databases, search engines and RDBMS. Learn scalability and responsiveness concepts that are usually ignored. Effectively balance scalability, performance, responsiveness, and availability while minimizing downtime. Who This Book Is For Executives (CXOs), software architects , developers, and IT Pros

Java for Data Science

Java for Data Science
Author: Richard Reese,Jennifer Reese
Publsiher: Unknown
Total Pages: 558
Release: 2016-12-30
Genre: Electronic Book
ISBN: 1785280112

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Examine the techniques and Java tools that are supporting the growing field of data scienceAbout This Book* Your entry ticket to the world of data science with the stability and power of Java* Explore, analyze, and visualize your data effectively using easy-to-follow examples* Make your Java applications smarter using machine learningWho This Book Is ForThis book is for Java developers who are comfortable with developing applications in Java. Those who now want to enter the world of data science or wish to build intelligent applications will find this book ideal. Aspiring data scientists will also find this book very helpful.What you will learn* Understand the nature and key concepts used in the field of data science* Grasp how data is collected, cleaned, and processed* Get to grips with key data analysis techniques* See specialized analysis techniques centered around machine learning* Master the effective visualization of your data* Work with the Java APIs and techniques used to perform data analysisIn DetailData science is concerned with extracting knowledge and insights from a wide variety of data sources to analyze patterns or predict future behavior. It draws from a wide array of disciplines including such fields as statistics, computer science, mathematics, machine learning, and data mining. In this book, we cover the important data science concepts and how they are supported by Java, as well as the often statistically challenging techniques so you understand their purpose and application.The book starts with an introduction of data science, followed by the basic data science tasks of data collection, data cleaning, data analysis, and data visualization. This is followed by a discussion of statistical techniques and then more advanced topics including machine learning, neural networks, and deep learning. The next section examines the major categories of data analysis including text, visual, and audio data.The book ends with a discussion on the resources that support the parallel implementation of many of these techniques and then a conclusion where more in-depth problem are illustrated. Due to the nature of the topic, simple examples of a technique are presented early followed by a more detailed treatment later in the book. This permits a more natural and smooth introduction to the techniques and flow in the book.

Data Structures and Algorithms Made Easy

Data Structures and Algorithms Made Easy
Author: Narasimha Karumanchi
Publsiher: Careermonk Publications
Total Pages: 428
Release: 2011-12
Genre: Computers
ISBN: 819210754X

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Peeling Data Structures and Algorithms for interviews [re-printed with corrections and new problems]: "Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles" is a book that offers solutions to complex data structures and algorithms. There are multiple solutions for each problem and the book is coded in C/C++, it comes handy as an interview and exam guide for computer scientists. A handy guide of sorts for any computer science professional, "Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles" is a solution bank for various complex problems related to data structures and algorithms. It can be used as a reference manual by those readers in the computer science industry. The book has around 21 chapters and covers Recursion and Backtracking, Linked Lists, Stacks, Queues, Trees, Priority Queue and Heaps, Disjoint Sets ADT, Graph Algorithms, Sorting, Searching, Selection Algorithms [Medians], Symbol Tables, Hashing, String Algorithms, Algorithms Design Techniques, Greedy Algorithms, Divide and Conquer Algorithms, Dynamic Programming, Complexity Classes, and other Miscellaneous Concepts. Data Structures And Algorithms Made Easy: Data Structure And Algorithmic Puzzles by Narasimha Karumanchi was published in March, and it is coded in C/C++ language. This book serves as guide to prepare for interviews, exams, and campus work. It is also available in Java. In short, this book offers solutions to various complex data structures and algorithmic problems. What is unique? Our main objective isn't to propose theorems and proofs about DS and Algorithms. We took the direct route and solved problems of varying complexities. That is, each problem corresponds to multiple solutions with different complexities. In other words, we enumerated possible solutions. With this approach, even when a new question arises, we offer a choice of different solution strategies based on your priorities. Topics Covered: IntroductionRecursion and BacktrackingLinked ListsStacksQueuesTreesPriority Queue and HeapsDisjoint Sets ADTGraph AlgorithmsSorting Searching Selection Algorithms [Medians] Symbol Tables Hashing String Algorithms Algorithms Design Techniques Greedy Algorithms Divide and Conquer Algorithms Dynamic Programming Complexity Classes Miscellaneous Concepts Target Audience? These books prepare readers for interviews, exams, and campus work. Language? All code was written in C/C++. If you are using Java, please search for "Data Structures and Algorithms Made Easy in Java." Also, check out sample chapters and the blog at: CareerMonk.com

Learning Spark

Learning Spark
Author: Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee
Publsiher: O'Reilly Media
Total Pages: 400
Release: 2020-07-16
Genre: Computers
ISBN: 9781492050018

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Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow

Python Made Easy

Python Made Easy
Author: Nilabh Nishchhal
Publsiher: Notion Press
Total Pages: 436
Release: 2020-10-20
Genre: Computers
ISBN: 9781649837264

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Python Made Easy: Beginners Guide to Programming and Data Analysis using Python Get comprehensive learning of Python Programming starting from the very basics and going up to utilizing python libraries for data analysis and Visualization. Based on the author’s journey to master Python, this book will help you to quickly start with writing programs and solving your problems using Python. It provides an ideal and elegant way to start learning Python, both for a newcomer to the programming world and a professional developer expert in other languages. This book comes loaded with illustrations and real-life examples. It gives you exercises which challenge you to refresh your conceptual clarity and write better codes. It is super easy to follow and will work as a self-paced tutorial to get you started with the latest and best in Python. All the advanced Python features to date are included. • Get to know the history, present, and future of Data Science • Get introduced to the basics of Computer Programming • Explore the exciting world of Python using Anaconda • Learn how to install and use Python on your computer • Create your Variables, Objects and learn Syntax of operations • Explore Python’s built-in object types like Lists, dictionaries, Tuples, Strings and sets • Learn to make your codes reusable by using functions • Organize your codes, functions and other objects into larger components with Modules • Explore Classes – the Object-Oriented Programming tool for elegant codes • Write complex codes and learn how to handle Errors and Exceptions • Learn about NumPy arrays and operations on them • Explore data analysis using pandas on a real-life data set • Dive into the exciting world of Visualization with 3 chapters on Visualization and Matplotlib • Experience the Power of What you learnt by 3 projects • Learn to make your own application complete with GUI by using API

The Data Science Handbook

The Data Science Handbook
Author: Field Cady
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2017-02-28
Genre: Mathematics
ISBN: 9781119092940

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A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Spark The Definitive Guide

Spark  The Definitive Guide
Author: Bill Chambers,Matei Zaharia
Publsiher: "O'Reilly Media, Inc."
Total Pages: 606
Release: 2018-02-08
Genre: Computers
ISBN: 9781491912294

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Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation