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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 do we care about predicting the future?
  2. What does seasonality mean? How could it impact our predictions?
  3. How does one measure the impact of seasonality?
  4. Write an R program to use the moving average of the last five years to predict the next year's expected return. The source of the data is http://fiannce.yahoo.com. You can test a few stocks such as IBM, C, and WMT. In addition, apply the same method to the S&P500 index. What is your conclusion?
  5. Assume that we have the following true model:

Write a Python program to use linear and polynomial models to approximate the previous function and show the related graphs.

  1. Download a market index monthly data and estimate its next year's annual return. The S&P500 could be used as the index and Yahoo!Finance at finance.yahoo.com could be used as the source of data. Source of data: https...
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