Shukla Haskell Data Analysis cookbook

Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes
1. Auflage 2025
ISBN: 978-1-78328-634-8
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 334 Seiten

Key FeaturesBook DescriptionStep-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.What you will learn - Obtain and analyze raw data from various sources including text files, CSV files, databases, and websites
- Implement practical tree and graph algorithms on various datasets
- Apply statistical methods such as moving average and linear regression to understand patterns
- Fiddle with parallel and concurrent code to speed up and simplify timeconsuming algorithms
- Find clusters in data using some of the most popular machine learning algorithms
- Manage results by visualizing or exporting data
Who this book is for

Autoren/Hrsg.

Shukla, Nishant

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Über Autor(innen)

Shukla Nishant :

Nishant Shukla is a computer scientist with a passion for mathematics. Throughout the years, he has worked for a handful of start-ups and large corporations including WillowTree Apps, Microsoft, Facebook, and Foursquare. Stepping into the world of Haskell was his excuse for better understanding Category Theory at first, but eventually, he found himself immersed in the language. His semester-long introductory Haskell course in the engineering school at the University of Virginia (http://shuklan.com/haskell) has been accessed by individuals from over 154 countries around the world, gathering over 45,000 unique visitors. Besides Haskell, he is a proponent of decentralized Internet and open source software. His academic research in the fields of Machine Learning, Neural Networks, and Computer Vision aim to supply a fundamental contribution to the world of computing.