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Scala Data Analysis Cookbook

You're reading from  Scala Data Analysis Cookbook

Product type Book
Published in Oct 2015
Publisher
ISBN-13 9781784396749
Pages 254 pages
Edition 1st Edition
Languages
Author (1):
Arun Manivannan Arun Manivannan
Profile icon Arun Manivannan

Table of Contents (14) Chapters

Scala Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Breeze 2. Getting Started with Apache Spark DataFrames 3. Loading and Preparing Data – DataFrame 4. Data Visualization 5. Learning from Data 6. Scaling Up 7. Going Further Index

About the Reviewers

Amir Hajian is a data scientist at the Thomson Reuters Data Innovation Lab. He has a PhD in astrophysics, and prior to joining Thomson Reuters, he was a senior research associate at the Canadian Institute for Theoretical Astrophysics in Toronto and a research physicist at Princeton University. His main focus in recent years has been bringing data science into astrophysics by developing and applying new algorithms for astrophysical data analysis using statistics, machine learning, visualization, and big data technology. Amir's research has been frequently highlighted in the media. He has led multinational research team efforts into successful publications. He has published in more than 70 peer-reviewed articles with more than 4,000 citations, giving him an h-index of 34.

Shams Mahmood Imam completed his PhD from the department of computer science at Rice University, working under Prof. Vivek Sarkar in the Habanero multicore software research project. His research interests mostly include parallel programming models and runtime systems, with the aim of making the writing of task-parallel programs on multicore machines easier for programmers. Shams is currently completing his thesis titled Cooperative Execution of Parallel Tasks with Synchronization Constraints. His work involves building a generic framework that efficiently supports all synchronization patterns (and not only those available in actors or the fork-join model) in task-parallel programs. It includes extensions such as Eureka programming for speculative computations in task-parallel models and selectors for coordination protocols in the actor model. Shams implemented a framework as part of the cooperative runtime for the Habanero-Java parallel programming library. His work has been published at leading conferences, such as OOPSLA, ECOOP, Euro-Par, PPPJ, and so on. Previously, he has been involved in projects such as Habanero-Scala, CnC-Scala, CnC-Matlab, and CnC-Python.

Gerald Loeffler is an MBA. He was trained as a biochemist and has worked in academia and the pharmaceutical industry, conducting research in parallel and distributed biophysical computer simulations and data science in bioinformatics. Then he switched to IT consulting and widened his interests to include general software development and architecture, focusing on JVM-centric enterprise applications, systems, and their integration ever since. Inspired by the practice of commercial software development projects in this context, Gerald has developed a keen interest in team collaboration, the software craftsmanship movement, sound software engineering, type safety, distributed software and system architectures, and the innovations introduced by technologies such as Java EE, Scala, Akka, and Spark. He is employed by MuleSoft as a principal solutions architect in their professional services team, working with EMEA clients on their integration needs and the challenges that spring from them.

Gerald lives with his wife and two cats in Vienna, Austria, where he enjoys music, theatre, and city life.

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