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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 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis

"It is a capital mistake to theorize before one has data."

Sherlock Holmes

In this chapter, we focus on a fundamental computer science data model called graphs and the different types of algorithm commonly used on them. As a data scientist or developer, it is very important to be familiar with graphs and quickly recognize when they provide the right solution to solve a particular data problem. For example, graphs are very well suited to GPS-based applications such as Google Maps, to find the best route from point A to point B, taking into account all kinds of parameters, including whether the user is driving, walking or taking public transport, or whether the user wants the shortest route or one that maximizes the use of highways regardless of overall distance. Some of these parameters can also be real-time parameters,...

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