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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. Why should we care about data visualization?
  2. Where can we find lists of R, Python, and Julia packages associated with data visualization?
  3. Draw a graph using R and Python for the following formula:
  1. Based on R programming, put the following two graphs together:
  2. Download the R dataset related to the Fama-French monthly factor time series at http://canisius.edu/~yany/RData/ff3monthly.RData. Then, draw the histograms for these three factors: Market, SMB, and HML.
  3. Write an R program to generate 1,000 random numbers from a uniform distribution. Then, estimate their mean and standard deviation. Finally, draw a histogram. Note that the R function for drawing n random numbers from a uniform distribution is runif(n).
  4. Repeat the previous exercise using Python and Julia.

  1. Use Python to draw both the sine and cosine functions together.
  2. From a beta distribution...
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