Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / Nishant Shukla ; cover image by Jarek Blaminsky.

Format:

Book

Author/Creator:

Shukla, Nishant, author.

Contributor:

Blaminsky, Jarek, cover designer.

Series:

Quick answers to common problems

Language:

English

Subjects (All):

Haskell (Computer program language).

Physical Description:

1 online resource (334 p.)

Edition:

1st edition

Place of Publication:

Birmingham, [England] : Packt Publishing, 2014.

Language Note:

English

System Details:

text file

Biography/History:

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.

Summary:

Step-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.

Contents:

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Hunt for Data; Introduction; Harnessing data from various sources; Accumulating text data from a file path; Catching I/O code faults; Keeping and representing data from a CSV file; Examining a JSON file with the aeson package; Reading an XML file using the HXT package; Capturing table rows from an HTML page; Understanding how to perform HTTP GET requests; Learning how to perform HTTP POST requests; Traversing online directories for data

Using MongoDB queries in HaskellReading from a remote MongoDB server; Exploring data from a SQLite database; Chapter 2: Integrity and Inspection; Introduction; Trimming excess whitespace; Ignoring punctuation and specific characters; Coping with unexpected or missing input; Validating records by matching regular expressions; Lexing and parsing an e-mail address; Deduplication of nonconflicting data items; Deduplication of conflicting data items; Implementing a frequency table using Data.List; Implementing a frequency table using Data.MultiSet; Computing the Manhattan distance

Computing the Euclidean distanceComparing scaled data using the Pearson correlation coefficient; Comparing sparse data using cosine similarity; Chapter 3: The Science of Words; Introduction; Displaying a number in another base; Reading a number from another base; Searching for a substring using Data.ByteString; Searching a string using the Boyer-Moore-Horspool algorithm; Searching a string using the Rabin-Karp algorithm; Splitting a string on lines, words, or arbitrary tokens; Finding the longest common subsequence; Computing a phonetic code; Computing the edit distance

Computing the Jaro-Winkler distance between two stringsFinding strings within one-edit distance; Fixing spelling mistakes; Chapter 4: Data Hashing; Introduction; Hashing a primitive data type; Hashing a custom data type; Running popular cryptographic hash functions; Running a cryptographic checksum on a file; Performing fast comparisons between data types; Using a high-performance hash table; Using Google's CityHash hash functions for strings; Computing a Geohash for location coordinates; Using a bloom filter to remove unique items; Running MurmurHash, a simple but speedy hashing algorithm

Measuring image similarity with perceptual hashesChapter 5: The Dance with Trees; Introduction; Defining a binary tree data type; Defining a rose tree (multiway tree) data type; Traversing a tree depth-first; Traversing a tree breadth-first; Implementing a Foldable instance for a tree; Calculating the height of a tree; Implementing a binary search tree data structure; Verifying the order property of a binary search tree; Using a self-balancing tree; Implementing a min-heap data structure; Encoding a string using a Huffman tree; Decoding a Huffman code; Chapter 6: Graph Fundamentals

Introduction

Notes:

"Quick answers to common problems"--Cover.

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed July 10, 2014).

ISBN:

9781783286348

1783286342

OCLC:

882610624