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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

Chapter 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis

	"Data is the new oil."
	--Unknown

In this chapter we are going to look at two important fields of AI and data science: natural language processing (NLP) and big data analysis. For the supporting sample application, we re-implement the Sentiment analysis of Twitter hashtags project described in Chapter 1, Programming and Data Science – A New Toolset, but this time we leverage Jupyter Notebooks and PixieDust to build live dashboards that analyze data from a stream of tweets related to a particular entity, such as a product offered by a company, for example, to provide sentiment information as well as information about other trending entities extracted from the same tweets. At the end of this chapter, the reader will learn how to integrate cloud-based NLP services such as IBM Watson Natural Language Understanding into their application as well as perform data...

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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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