Haskell: Data Analysis Made Easy

More Information
Learn
  • Understand the basic concepts of data analysis
  • Create Haskell functions for the common descriptive statistics functions
  • Learn to apply regular expressions in large-scale datasets
  • Plot data with the gnuplot tool and the EasyPlot library
  • Reduce the size of data without affecting the data’s effectiveness using Principal Component Analysis
  • Master the techniques necessary to perform multivariate regression using Haskell code
About

A staggering amount of data is created everyday; analyzing and organizing this enormous amount of data can be quite a complex task. Haskell is a powerful and well-designed functional programming language that is designed to work with complex data. It is trending in the field of data science as it provides a powerful platform for robust data science practices.

This course will introduce the basic concepts of Haskell and move on to discuss how Haskell can be used to solve the issues by using the real-world data.

The course will guide you through the installation procedure, after you have all the tools that you require in place, you will explore the basic concepts of Haskell including the functions, and the data structures.

It will also discuss the various formats of raw data and the procedures for cleaning the data and plotting them.

With a good hold on the basics of Haskell and data analysis, you will then be introduced to advanced concepts of data analysis such as Kernel Density Estimation, Hypothesis testing, Regression analysis, text analysis, clustering, Naïve Bayes Classification, and Principal Component Analysis.

After completing this course, you will be equipped to analyze data and organize them using advanced algorithms.

Style and Approach:

The integrated course follows a step-by-step approach that uses real-world data and examples to build on the concepts covered.

This course is a blend of text, videos, code examples, and assessments, all packaged up keeping your journey in mind. The curator of this course has combined some of the best that Packt has to offer in one complete package.

DISCLAIMER

This course combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

Note: This interactive EPUB adheres to the latest specification, and requires that your reader supports video and interactive content. We recommend using Readium with the latest stable version of Google Chrome, or iBooks for OS X.

Features
  • Set up the Haskell environment
  • Perform meaningful analysis on real-world data using the Haskell language
  • Visualize and harvest information from data
  • Use advanced data analysis concepts to identify patterns in the data
Course Length 7 hours 30 mins
ISBN 9781787283633
Date Of Publication 28 Feb 2017

Authors

James Church

James Church lives in Clarksville, Tennessee, United States, where he enjoys teaching, programming, and playing board games with his wife, Michelle. He is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work. James is the author of Learning Haskell Data Analysis.

Hakim Cassimally

Hakim Cassimally learned the basics of Lisp 15 years ago and has been interested in functional programming ever since. After Audrey Tang developed the first prototype of Perl6 in Haskell (Pugs), he got seriously interested in Haskell and has written, spoken, and evangelised about learning and writing Haskell since 2006.

Even when developing in other functional languages such as XQuery or traditional scripting languages such as Perl or Python, lessons learned from Haskell inform his approach and prototypes—whether it’s training software for a start-up, just-in-time sequencing systems for a car manufacturer, or data imports for a national media corporation.

His latest personal Haskell project is a Cryptic Crossword solver.

https://www.linkedin.com/in/hakim-cassimally-4144a51

https://vimeo.com/24676617