Synopsis
Designed by a committee of more than a dozen brilliant researchers, Haskell is one of the fastest growing languages in terms of both usage and popularity. A powerful functional programming language with a strong type system and parallelism support, Haskell is just the tool for harnessing the patterns behind the corpus of data.
The Haskell Data Analysis Cookbook provides recipes covering every stage of the data analysis process. Whether obtaining and cleaning data, performing real-time analysis, or finding the best forms of visualization the book provides focused and practical advice on using Haskell as the best tool for the job.
Topics covered include reading and accessing data from a variety of file formats, cleaning the data for analysis, constructing tree data structures, clustering, and graphing and visualizing the products of your analysis.
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About the Author
Nishant Shukla
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.
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Haskell Data Analysis Cookbook: Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes
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Digital. Condition: New. 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 learnObtain and analyze raw data from various sources including text files, CSV files, databases, and websitesImplement practical tree and graph algorithms on various datasetsApply statistical methods such as moving average and linear regression to understand patternsFiddle with parallel and concurrent code to speed up and simplify timeconsuming algorithmsFind clusters in data using some of the most popular machine learning algorithmsManage results by visualizing or exporting dataWho this book is for. Seller Inventory # LU-9781783286331
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