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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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Toc

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

Review questions and exercises

  1. What is distributed computing? Why it is useful?
  2. From where could we get a task view for parallel computing?
  3. From the task view related to parallel computing, we can find many R packages. Identify a few of them. Install two and find a few examples of using these two packages.
  4. Conduct a word frequency analysis using: The Count of Monte Cristo by Alexandre Dumas (input file is at http://www.gutenberg.org/files/1184/1184-0.txt).
  5. From where could we find more information about Anaconda add-ons?
  6. What is HPCC and how does it work?
  7. How do we find the path of an installed R package?
  8. In the sample Jupyter notebook about parallel Monte-Carlo options pricing, the related Asian options are defined here, where call(Asian) is the Asian put option, Put(Asian), K is the exercise price, and is the average price over the path:

Write a Jupyter notebook to use...

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