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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

Introducing PixieDust

Tip

Fun fact

I am often asked how I came up with the name PixieDust, for which I answer that I simply wanted to make Notebook simple, as in magical, for non-data scientists.

PixieDust (https://github.com/ibm-watson-data-lab/pixiedust) is an open-source project composed primarily of three components designed to address the three goals stated at the beginning of this chapter:

  • A helper Python library for Jupyter Notebooks that provides simple APIs to load data from various sources into popular frameworks, such as pandas and Apache Spark DataFrame, and then to visualize and explore the dataset interactively.
  • A simple Python-based programming model that enables developers to "productize" the analytics directly into the Notebook by creating powerful dashboards called PixieApps. As we'll see in the next chapters, PixieApps are different from traditional BI (short for, Business Intelligence) dashboards because developers can directly use HTML and...
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