Haskell Data Analysis Cookbook
- نوع فایل : کتاب
- زبان : انگلیسی
- نویسنده : Nishant Shukla
- چاپ و سال / کشور: 2014
- تعداد صفحه : 288
- شابک / ISBN : 1783286334
Description
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.
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 time-consuming algorithms
Find clusters in data using some of the most popular machine learning algorithms
Manage results by visualizing or exporting data
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
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 time-consuming algorithms
Find clusters in data using some of the most popular machine learning algorithms
Manage results by visualizing or exporting data
