Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Nataraj Dasgupta

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商品描述

Get command of your organizational Big Data using the power of data science and analytics

Key Features

  • A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions
  • Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses
  • Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data

Book Description

Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that.

With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks.

By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book.

What you will learn

  • Get a 360-degree view into the world of Big Data, data science and machine learning
  • Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives
  • Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R
  • Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions
  • Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications
  • Understand corporate strategies for successful Big Data and data science projects
  • Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies

Who This Book Is For

The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

Table of Contents

  1. Too Big Or Not Too Big
  2. Big Data Mining For The Masses
  3. From Big Data to Data Analytics
  4. Big Data Mining & Hadoop
  5. Big Data Mining & NoSQL
  6. Big Data Mining & Spark
  7. Machine Learning For The Masses
  8. Machine Learning Deep Dive
  9. The Analytics Infrastructure
  10. Closing thoughts on Big Data
  11. Appendix

商品描述(中文翻譯)

掌握組織的大數據,利用數據科學和分析的力量

主要特點:
- 提升大數據存儲、處理和分析技能,幫助您做出明智的業務決策
- 使用Apache Hadoop、R、Python和Spark等最佳工具,進行大規模在線分析
- 獲得關於大數據的統計推斷、機器學習、數學建模和數據可視化的專家建議

書籍描述:
大數據分析是組織使用的策略,用於收集、組織和分析大量數據,以揭示傳統系統無法分析的有價值的業務洞察。構建一個企業級的成本效益高的大數據和機器學習解決方案,以從組織的數據中發現洞察和價值是一個挑戰。如今,隨著數百種新的大數據系統、機器學習套件和商業智能工具的出現,選擇合適的技術組合變得更加困難。本書將幫助您解決這個問題。

通過本指南的幫助,您將能夠填補技術理論世界與實際建立企業級大數據和數據科學平台之間的差距。您將親身體驗Hadoop和Spark,使用R和R Shiny構建機器學習儀表板,使用NoSQL數據庫(如MongoDB)創建基於Web的應用程序,甚至學習如何為神經網絡編寫R代碼。

通過閱讀本書,您將對大數據分析的含義有非常清晰和具體的理解,了解它如何為組織帶來收入,以及如何使用本書中介紹的不同工具和方法開發自己的大數據分析解決方案。

學到什麼:
- 從360度的角度了解大數據、數據科學和機器學習的世界
- 涵蓋技術和商業大數據分析主題,滿足技術專家和企業IT高管的興趣
- 使用行業標準的大數據和機器學習工具,如Hadoop、Spark、MongoDB、KDB+和R,獲得實踐經驗
- 使用逐步指南創建生產級機器學習商業智能儀表板,使用R和R Shiny
- 學習如何結合開源大數據、機器學習和商業智能工具,創建低成本的業務分析應用程序
- 了解成功的大數據和數據科學項目的企業策略
- 利用新興技術開發尖端的大數據應用程序,超越通用目的的分析

適合閱讀對象:
本書適合現有和有志於成為組織中大數據架構、分析和治理專家的人士。雖然不需要對大數據或相關技術有先備知識,但具備一些編程經驗會有所幫助。

目錄:
1. 大還是不大
2. 大數據挖掘
3. 從大數據到數據分析
4. 大數據挖掘和Hadoop
5. 大數據挖掘和NoSQL
6. 大數據挖掘和Spark
7. 大眾機器學習
8. 深入機器學習
9. 分析基礎設施
10. 對大數據的結論
11. 附錄