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Data Analysis with Python

You're reading from   Data Analysis with Python A Modern Approach

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789950069
Length 490 pages
Edition 1st Edition
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Author (1):
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David Taieb David Taieb
Author Profile Icon David Taieb
David Taieb
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Table of Contents (14) Chapters Close

Preface 1. Programming and Data Science – A New Toolset FREE CHAPTER 2. Python and Jupyter Notebooks to Power your Data Analysis 3. Accelerate your Data Analysis with Python Libraries 4. Publish your Data Analysis to the Web - the PixieApp Tool 5. Python and PixieDust Best Practices and Advanced Concepts 6. Analytics Study: AI and Image Recognition with TensorFlow 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis 10. The Future of Data Analysis and Where to Develop your Skills A. PixieApp Quick-Reference Other Books You May Enjoy Index

Chapter 4. Publish your Data Analysis to the Web - the PixieApp Tool

"Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories."

Steven Levitt, co-author of Freakonomics

In the previous chapter, we discussed how Jupyter Notebooks, coupled with PixieDust, accelerate your data science projects with simple APIs that let you load, clean, and visualize data without the need to write extensive code, as well as enable collaboration between data scientists and developers with PixieApps. In this chapter, we'll show how to liberate your PixieApps and associated data analytics from the Jupyter Notebook by publishing them as web applications using the PixieGateway server. This operationalization of the Notebook is particularly attractive to the line of business user persona (business analysts, C-Suite executives, and many more...

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