Bugnion - Scala for data science leverage the power of Scala to build scalable, robust data science applications
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Scala for data science leverage the power of Scala to build scalable, robust data science applications: summary, description and annotation
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Leverage the power of Scala with different tools to build scalable, robust data science applications
About This Book
- Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations
- Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided
Who This Book Is For
If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions.
What You Will Learn
- Transform and filter tabular data to extract features for machine learning
- Implement your own algorithms or take advantage of MLLibs extensive suite of models to build distributed machine learning pipelines
- Read, transform, and...
Bugnion: author's other books
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![Pascal Bugnion [Pascal Bugnion] - Scala for Data Science](/uploads/posts/book/119601/thumbs/pascal-bugnion-pascal-bugnion-scala-for-data.jpg)
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: January 2016
Production reference: 1220116
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78528-137-2
www.packtpub.com
Author
Pascal Bugnion
Reviewers
Umanga Bista
Radek Ostrowski
Yuanhang Wang
Commissioning Editor
Veena Pagare
Acquisition Editor
Sonali Vernekar
Content Development Editor
Shali Deeraj
Technical Editor
Suwarna Patil
Copy Editor
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Indexer
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Graphics
Disha Haria
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Cover Work
Arvindkumar Gupta
Pascal Bugnion is a data engineer at the ASI, a consultancy offering bespoke data science services. Previously, he was the head of data engineering at SCL Elections. He holds a PhD in computational physics from Cambridge University.
Besides Scala, Pascal is a keen Python developer. He has contributed to NumPy, matplotlib and IPython. He also maintains scikit-monaco, an open source library for Monte Carlo integration. He currently lives in London, UK.
I owe a huge debt of gratitude to my parents and my partner for supporting me in this, as well as my employer for encouraging me to pursue this project. I also thank the reviewers, Umanga Bista, Yuanhang Wang, and Radek Ostrowski for their tireless efforts, as well as the entire team at Packt for their support, advice, and hard work carrying this book to completion.
Umanga Bista is machine learning and real-time analytics enthusiast from Kathmandu. He completed his bachelors in computer engineering in September, 2013. Since then, he has been working at LogPoint, a SEIM product and company. He primarily works on building statistical plugins and real time, scalable, and fault tolerant architecture to process multiterabyte scale log data streams for security analytics, intelligence, and compliance.
Radek Ostrowski is a freelance big data engineer with an educational background in high-performance computing. He specializes in building scalable real-time data collection and predictive analytics platforms. He has worked at EPCC, University of Edinburgh in data-related projects for many years. Additionally, he has contributed to the success of a game's startupdeltaDNA, co-built super-scalable backend for PlayStation 4 at Sony, helped to improve data processes at Expedia, and started a Docker revolution at Tesco Bank. He is currently working with Spark and Scala for Max2 Inc, an NYC-based startup that is building a community-powered venue discovery platform, offering personalized recommendations, curated and real-time information.
Yuanhang Wang is a data scientist with primary focus on DSL design. He has dabbled in several functional programming languages. He is particularly interested in machine learning and programming language theory. He is currently a data scientist at China Mobile Research Center, working on typed data processing engine and optimizer that is built on top of several big-data platforms.
Yuanhang Wang describes himself as an enthusiast of purely functional programming and neural networks. He obtained his master's degrees both in Harbin Institute of Technology, China and University of Pavia, Italy.
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To my parents.
To Jessica and to my friends.
Data science is fashionable. Data science startups are sprouting across the globe and established companies are scrambling to assemble data science teams. The ability to analyze large datasets is also becoming increasingly important in the academic and research world.
Why this explosion in demand for data scientists? Our view is that the emergence of data science can be viewed as the serendipitous collusion of several interlinked factors. The first is data availability. Over the last fifteen years, the amount of data collected by companies has exploded. In the world of research, cheap gene sequencing techniques have drastically increased the amount of genomic data available. Social and professional networking sites have built huge graphs interlinking a significant fraction of the people living on the planet. At the same time, the development of the World Wide Web makes accessing this wealth of data possible from almost anywhere in the world.
The increased availability of data has resulted in an increase in data awareness. It is no longer acceptable for decision makers to trust their experience and "gut feeling" alone. Increasingly, one expects business decisions to be driven by data.
Finally, the tools for efficiently making sense of and extracting insights from huge data sets are starting to mature: one doesn't need to be an expert in distributed computing to analyze a large data set any more. Apache Spark, for instance, greatly eases writing distributed data analysis applications. The explosion of cloud infrastructure facilitates scaling computing needs to cope with variable data amounts.
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