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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Summary


In this chapter, we learned to visualize in Julia using different graphics options.

We studied the PyPlot and how we can exploit the vast matplotlib library. We worked on various examples. We also went through Unicode plots, which are very lightweight and can be used in the terminal. Vega and Gadfly, the two most popular graphics libraries, were also explained in this chapter. Using different plots such as scatter, line, box, histogram, bar, and violin, we understood how and why it is important and helpful to visualize our data.

In the next chapter, we will study machine learning with Julia.

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