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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

Anatomy of a PixieApp

Note

Note: The PixieApp programming model doesn't require any prior experience with JavaScript, however, it is expected that the reader is familiar with the following:

The term PixieApp stands for Pixie Application, and is meant to emphasize its tight integration with the PixieDust capabilities, especially the display() API. Its main goal is to make it easy for developers to build a user interface that can invoke the data analytics implemented in the Jupyter Notebook.

A PixieApp follows the single-page application (SPA) design pattern (https://en.wikipedia.org/wiki/Single-page_application), where the user is presented with a welcome screen that is dynamically updated to respond to a user interaction. An update can be a partial refresh, such as updating a graph after the user clicks on a control or a full refresh, such as a new screen in a multistep...

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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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