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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

Why choose Python?

Like many developers, when it came to building data-intensive projects, using Python wasn't my first choice. To be honest, having worked with Java for so many years, Scala seemed much more attractive to me at first, even though the learning curve was pretty steep. Scala is a very powerful language that elegantly combines object-oriented and functional programming, which is sorely lacking in Java (at least until Java 8 started to introduce lambda expressions).

Scala also provides a very concise syntax that translates into fewer lines of code, higher productivity, and ultimately fewer bugs. This comes in very handy, especially when a large part of your work is to manipulate data. Another reason for liking Scala is the better API coverage when using big data frameworks such as Apache Spark, which are themselves written in Scala. There are also plenty of other good reasons to prefer Scala, such as it's a strong typed system and its interoperability with...

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