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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda FREE CHAPTER 2. Anaconda Installation 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Introduction to distributed versus parallel computing

Distributed computing is a subfield of computer science that studies distributed systems and models in which components located on networked computers communicate and coordinate their actions by passing messages. The components interact with each other in order to achieve a common goal.

It is worthwhile to discuss another phrase: parallel computing. Parallel computing is more tightly coupled to multi-threading, or how to make full use of a single CPU, while distributed computing refers to the notion of divide and conquer, executing subtasks on different machines, and then merging the results.

Since we have entered a so-called big data era, it seems that the distinction is melting. In fact, nowadays, many systems use a combination of parallel and distributed computing.

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