Spark for Data Science - Couverture souple

Singhal, Bikramaditya; Duvvuri, Srinivas

Couverture souple

ISBN 10 : 1785885650 ISBN 13 : 9781785885655

Editeur : Packt Publishing, 2016

A propos du livre

Présentation de l'éditeur

Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

About This Book

• Perform data analysis and build predictive models on huge datasets that leverage Apache Spark
• Learn to integrate data science algorithms and techniques with the fast and scalable computing features of Spark to address big data challenges
• Work through practical examples on real-world problems with sample code snippets

Who This Book Is For

This book is for anyone who wants to leverage Apache Spark for data science and machine learning. If you are a technologist who wants to expand your knowledge to perform data science operations in Spark, or a data scientist who wants to understand how algorithms are implemented in Spark, or a newbie with minimal development experience who wants to learn about Big Data Analytics, this book is for you!

What You Will Learn

• Consolidate, clean, and transform your data acquired from various data sources
• Perform statistical analysis of data to find hidden insights
• Explore graphical techniques to see what your data looks like
• Use machine learning techniques to build predictive models
• Build scalable data products and solutions
• Start programming using the RDD, DataFrame and Dataset APIs
• Become an expert by improving your data analytical skills

In Detail

This is the era of Big Data. The words ‘Big Data’ implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages.

Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R.

With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.

Style and approach

This book takes a step-by-step approach to statistical analysis and machine learning, and is explained in a conversational and easy-to-follow style. Each topic is explained sequentially with a focus on the fundamentals as well as the advanced concepts of algorithms and techniques. Real-world examples with sample code snippets are also included.

Biographie de l'auteur

Srinivas Duvvuri Srinivas Duvvuri is currently Senior Vice President Development, heading the development teams for Fixed Income Suite of products at Broadridge Financial Solutions (India) Pvt Ltd. In addition, he also leads the Big Data and Data Science COE and is the principal member of the Broadridge India Technology Council. He is self learnt Data Scientist. The Big Data /Data Science COE in the past 3 years, has successfully completed multiple POC's and some of the use cases are moving towards production deployment. He has over 25+ years of experience in software product development. His experience spans predominantly in product development in, multiple domains Financial Services, Infrastructure Management, OLAP, Telecom Billing and Customer Care, CAD/CAM. Prior to Broadridge, he’s held leadership positions at a Startup and leading IT majors such as CA, Hyperion (Oracle), Globalstar. He has a patent in Relational OLAP. Srinivas loves to teach and mentor budding Engineers. He has established strong Academic connect and interacts with a host of educational institutions, He is an active speaker in various conferences, summits and meetups on topics such as Big data, Data Science Srinivas is a B.Tech in Aeronautical Engineering and M.Tech in Computer Science, from IIT, Madras.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2016
• ISBN 10: 1785885650
• ISBN 13: 9781785885655
• Reliure: Broché
• Langue: anglais
• Nombre de pages: 344