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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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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

Creating a set of programs in R and Python

On numerous occasions, for a specific research topic, researchers will collect many datasets and write many programs. Why not write a big program? There are several reasons for not doing so. First, we might need several steps to finish the project. Second, the project might be too complex, so we have divided the whole project into several small portions. Each researcher will be responsible for one or a few portions. Third, according to the flow of the whole process, we might want to have several parts, such as devoting a part to processing data, a part to running various regressions, and a part to summarizing the results. Because of this, we need a way of putting all the programs together. In the following example, we will show you how to achieve this in both R and Python. For R, assume that we have the following functions:

pv_f<-function...
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