Key Features
Book Description
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.
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
Table of Contents
- Obtaining Data
- Cleaning Data
- String Manipulations
- Hashing Data
- Tree Data Structure
- Graph Data Structure
- Statistical Methods
- Clustering
- Parallel Design
- Real Time Data Analysis
- Visualizing Data
- Evoking External Programs
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