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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

Summary

In this chapter, we have explained many important issues related to statistics, such as T-distribution, F-distribution, T-tests, F-tests, and other hypothesis tests. We have also discussed linear regression, how to deal with missing data, how to treat outliers, collinearity and its treatments, and how to run a multi-variable linear regression.

In Chapter 6, Managing Packages, we will discuss the importance of managing package; how to find out about all packages available for R, Python, and Julia; and how to find the manual for each package. In addition, we will discuss the issue of package dependency and how to make our programming a little easier when dealing with packages.

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