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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 the difference between open data and proprietary databases?
  2. Is it enough for learners in the area of data science to use open data?
  3. Where can we access open public data?
  4. From The UCI Data Depository, http://archive.ics.uci.edu/ml/index.php, download a dataset called Wine. Write a program in R to import it.
  5. From the UCI Data Depository, download a dataset called Forest Fire. Write a program in Python to import it.
  6. From the UCI Data Depository, download a dataset called Bank Marketing. Write a program in Octave to import it. Answer the following questions: 1) How many banks? and 2) What is the cost?
  7. How can we find all R functions with read. as their leading letters? (Note that there is a dot after read.)
  8. How can we find more information on an R function called read.xls()?
  9. Explain the differences between two R functions: save() and saveRDS...
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