Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipesOverviewA practical and concise guide to using Haskell when getting to grips with data analysisRecipes for every stage of data analysis, from collection to visualizationIn-depth examples demonstrating various tools, solutions and techniquesIn DetailThis book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques.You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.What you will learn from this bookObtain 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 time-consuming algorithmsFind clusters in data using some of the most popular machine learning algorithmsManage results by visualizing or exporting dataApproachStep-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code.Who this book is written forThis 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. 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 Haskell Reading 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 distance Comparing 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 strings Finding 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 hashes Chapter 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 Representing a graph from a list of edges Representing a graph from an adjacency list Conducting a topological sort on a graph Traversing a graph depth-first Traversing a graph breadth-first Visualizing a graph using Graphviz Using Directed Acyclic Word Graphs Working with hexagonal and square grid networks Finding maximal cliques in a graph Determining whether any two graphs are isomorphic Chapter 7: Statistics and Analysis Introduction Calculating a moving average Calculating a moving median Approximating a linear regression Approximating a quadratic regression Obtaining the covariance matrix from samples Finding all unique pairings in a list Using the Pearson correlation coefficient Evaluating a Bayesian network Creating a data structure for playing cards Using a Markov chain to generate text Creating n-grams from a list Creating a neural network perceptron Chapter 8: Clustering and Classification Introduction Implementing the k-means clustering algorithm Implementing hierarchical clustering Using a hierarchical clustering library Finding the number of clusters Clustering words by their lexemes Classifying the parts of speech of words Identifying key words in a corpus of text Training a parts of speech tagger Implementing a decision tree classifier Implementing a k-Nearest Neighbors classifier Visualizing points using Graphics.EasyPlot Chapter 9: Parallel and Concurrent Design Introduction Using the Haskell Runtime System options Evaluating a procedure in parallel Controlling parallel algorithms in sequence Forking I/O actions for concurrency Communicating with a forked I/O action Killing forked threads Parallelizing pure functions using the Par monad Mapping over a list in parallel Accessing tuple elements in parallel Implementing MapReduce to count word frequencies Manipulating images in parallel using Repa Benchmarking runtime performance in Haskell Using the criterion package to measure performance Benchmarking runtime performance in the terminal Chapter 10: Real-time Data Introduction Streaming Twitter for real-time sentiment analysis Reading IRC chat room messages Responding to IRC messages Polling a web server for latest updates Detecting real-time file directory changes Communicating in real time through sockets Detecting faces and eyes through a camera stream Streaming camera frames for template matching Chapter 11: Visualizing Data Introduction Plotting a line chart using Google's Chart API Plotting a pie chart using Google's Chart API Plotting bar graphs using Google's Chart API Displaying a line graph using gnuplot Displaying a scatter plot of two-dimensional points Interacting with points in a three-dimensional space Visualizing a graph network Customizing the looks of a graph network diagram Rendering a bar graph in JavaScript using D3.js Rendering a scatter plot in JavaScript using D3.js Diagramming a path from a list of vectors Chapter 12: Exporting and Presenting Introduction Exporting data to a CSV file Exporting data as JSON Using SQLite to store data Saving data to a MongoDB database Presenting results in an HTML web page Creating a LaTeX table to display results Personalizing messages using a text template Exporting matrix values to a file Index
In Detail
This book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques.
You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.
Approach
Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code.
Who this book is for
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.
In Detail This book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques. You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.Approach Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code.Who this book is for 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 Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes. This book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques. You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.
