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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 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting

"When making important decisions, it's ok to trust your instincts but always verify with data"

David Taieb

The study of time series is a very important field of data science with multiple applications in industry, including the weather, medicine, sales, and, of course, finance. It is a broad and complex subject and covering it in detail would be outside the scope of this book, but we'll try to touch upon a few of the important concepts in this chapter, staying sufficiently high level as not to require any particular specific knowledge from the reader. We also show how Python is particularly well adapted to time series analysis from data manipulation with libraries like pandas (https://pandas.pydata.org) for data analysis and NumPy (http://www.numpy.org) for scientific computation...

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