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Data Analysis with Python

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

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789950069

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

David Taieb

View More author details

Table of Contents (14) Chapters

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

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Index

Summary

After reading this chapter, you should be able to install, configure, and manage a PixieGateway microservice server, publish charts as a web page, and deploy a PixieApp from a Notebook to a web application. Whether you are a data scientist working on analytics in a Jupyter Notebook or a developer writing and deploying applications targeted at the line of a business user, we've shown, in this chapter, how PixieDust can help accomplish your tasks more efficiently and reduce the time it takes to operationalize your analytics.

In the next chapter, we'll look at advanced topics and best practices related to PixieDust and the PixieApp programming model, which will be useful when we go over the industry use cases and sample data pipelines in the remaining chapters.

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Authors (1)

David Taieb

David Taieb

David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.

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