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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 2. Anaconda Installation FREE CHAPTER 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 Anaconda and how do we use its platform?
  2. How many open source packages are accompanied with Anaconda?
  3. What is the home page for Anaconda?
  4. How do we install Anaconda? After Anaconda is installed, should we install Python separately? What about R?
  5. What is the size of a full Anaconda installation?
  6. Why should we care about Miniconda?
  7. What is Jupyter? How do we launch it without installation?
  8. What are the advantages and disadvantages of using https://jupyter.org/try?
  9. Where could a new learner find more useful information about Anaconda?
  10. Get more information about the Julia programming language.

  1. How do we write a simple program in Julia via Jupyter?
  2. How do we write a simple program in R via Jupyter?
  3. How do we find help for Jupyter?
  4. What is the conda Cheat Sheet and where can we download it?
  5. Could we run a simple R program without installing Anaconda?
  6. Could we run Anaconda without pre-installing it?
  7. Try the following two lines of Python code:
import numpy as np
print(np.sqrt(2))
  1. Try the following simple code for R:
x<-1:500
mean(x)
sd(x)
  1. Try the following code for Julia:
x=1:500
mean(x)
  1. Try the following code for R:
dd<-Sys.Date()
dd+40

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