Learning Spark

Author: Holden Karau
Publisher: "O'Reilly Media, Inc."
ISBN: 1449359051
Size: 51.68 MB
Format: PDF, Mobi
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Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables

Computational Intelligence In Data Mining

Author: Himansu Sekhar Behera
Publisher: Springer
ISBN: 9811038740
Size: 52.86 MB
Format: PDF, ePub
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The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science.

Essentials Of Business Analytics

Author: Bhimasankaram Pochiraju
Publisher: Springer
ISBN: 3319688375
Size: 34.60 MB
Format: PDF, Kindle
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This comprehensive volume is the first of its kind which serves as a textbook for long duration business analytics courses and as a perfect guide to the practitioner. This is an edited volume. The chapters are written by experts from the Indian School of Business, top US universities and industry. Every chapter has a business orientation. Typically, each chapter begins with business problems that are transformed into data questions; methodology is developed to solve the data question. Data analysis is done using widely used software, the output and results are clearly explained at each stage of development. Finally, the solution is transformed into a business solution. The editors have taken extreme care to ensure continuity across the chapters. The book has three parts: A) Methodology, B) Applications and C) Case Studies. In part A, the methodology is developed in detail. In part B, these methodologies are applied to solve business problems in various verticals. Part C contains case studies illustrating applications in a single instance. There is an appendix that develops the pre-requisites for the main text.

Data Mining And Big Data

Author: Ying Tan
Publisher: Springer
ISBN: 3319618458
Size: 51.37 MB
Format: PDF, Mobi
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This book constitutes the refereed proceedings of the Second International Conference on Data Mining and Big Data, DMBD 2017, held in Fukuoka, Japan, in July/August 2017. The 53 papers presented in this volume were carefully reviewed and selected from 96 submissions. They were organized in topical sections named: association analysis; clustering; prediction; classification; schedule and sequence analysis; big data; data analysis; data mining; text mining; deep learning; high performance computing; knowledge base and its framework; and fuzzy control.

Apache Spark For Data Science Cookbook

Author: Padma Priya Chitturi
Publisher: Packt Publishing Ltd
ISBN: 1785288806
Size: 33.13 MB
Format: PDF, ePub, Mobi
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Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.

Knowledge Based Intelligent Information And Engineering Systems

Author: Rajiv Khosla
Publisher: Springer
ISBN: 3540319867
Size: 49.36 MB
Format: PDF, ePub
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The four volume set LNAI 3681, LNAI 3682, LNAI 3683, and LNAI 3684 constitute the refereed proceedings of the 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, held in Melbourne, Australia in September 2005. The 716 revised papers presented were carefully reviewed and selected from nearly 1400 submissions. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense. The second volume contains papers on machine learning, immunity-based systems, medical diagnosis, intelligent hybrid systems and control, emotional intelligence and smart systems, context-aware evolvable systems, intelligent fuzzy systems and control, knowledge representation and its practical application in today's society, approaches and methods into security engineering, communicative intelligence, intelligent watermarking algorithms and applications, intelligent techniques and control, e-learning and ICT, logic based intelligent information systems, intelligent agents and their applications, innovations in intelligent agents, ontologies and the semantic web, knowledge discovery in data streams, computational intelligence tools techniques and algorithms, watermarking applications, multimedia retrieval, soft computing approach to industrial engineering, and experience management and information systems.