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Haskell Data Analysis cookbook

You're reading from   Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783286331
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Nishant Shukla Nishant Shukla
Author Profile Icon Nishant Shukla
Nishant Shukla
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Table of Contents (14) Chapters Close

Preface 1. The Hunt for Data FREE CHAPTER 2. Integrity and Inspection 3. The Science of Words 4. Data Hashing 5. The Dance with Trees 6. Graph Fundamentals 7. Statistics and Analysis 8. Clustering and Classification 9. Parallel and Concurrent Design 10. Real-time Data 11. Visualizing Data 12. Exporting and Presenting Index

Preface

Data analysis is something that many of us have done before, maybe even without knowing it. It is the essential art of gathering and examining pieces of information to suit a variety of purposes—from visual inspection to machine learning techniques. Through data analysis, we can harness the meaning from information littered all around the digital realm. It enables us to resolve the most peculiar inquiries, perhaps even summoning new ones in the process.

Haskell acts as our conduit for robust data analysis. For some, Haskell is a programming language reserved to the most elite researchers in academia and industry. Yet, we see it charming one of the fastest growing cultures of open source developers around the world. The growth of Haskell is a sign that people are uncovering its magnificent functional pureness, resilient type safety, and remarkable expressiveness. Flip the pages of this book to see it all in action.

Haskell Data Analysis Cookbook is more than just a fusion of two entrancing topics in computing. It is also a learning tool for the Haskell programming language and an introduction to simple data analysis practices. Use it as a Swiss Army Knife of algorithms and code snippets. Try a recipe a day, like a kata for your mind. Breeze through the book for creative inspiration from catalytic examples. Also, most importantly, dive deep into the province of data analysis in Haskell.

Of course, none of this would have been possible without a thorough feedback from the technical editors, brilliant chapter illustrations by Lonku (http://lonku.tumblr.com), and helpful layout and editing support by Packt Publishing.

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