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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

Summary

In this chapter, we have discussed Anaconda Cloud. Some topics included the Jupyter Notebook in depth, different formats of the Jupyter Notebook, how to share notebooks with your partner, how to share different projects over different platforms, how to share your working environments, and how to replicate others' environments locally.

For the next chapter, we will discuss distributed computing and Anaconda Accelerate. When our data or tasks become more complex, we'll need a good system or a set of tools to process data and run a complex algorithm. For this purpose, distributed computing is one solution. In particular, we will explain many concepts, such as compute nodes, project add-ons, parallel processing advanced Python for data parallelism. In addition, we will give several examples showing how to use distributed computing.

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