Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R
內容描述
Get command of your organizational Big Data using the power of data science and analytics Key FeaturesA perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisionsWork with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analysesGet expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big DataBook DescriptionBig 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 learnGet a 360-degree view into the world of Big Data, data science and machine learningBroad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executivesGet hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and RCreate production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructionsLearn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applicationsUnderstand corporate strategies for successful Big Data and data science projectsGo beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologiesWho This Book Is ForThe 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 ContentsToo Big Or Not Too BigBig Data Mining For The MassesFrom Big Data to Data AnalyticsBig Data Mining & HadoopBig Data Mining & NoSQLBig Data Mining & SparkMachine Learning For The MassesMachine Learning Deep DiveThe Analytics InfrastructureClosing thoughts on Big DataAppendix
