Getting Started with Haskell Data Analysis
Put your data analysis techniques to work and generate publication-ready visualizations
Seiten
2018
Packt Publishing Limited (Verlag)
978-1-78980-286-3 (ISBN)
Packt Publishing Limited (Verlag)
978-1-78980-286-3 (ISBN)
Data analysis is a part of computer science and part statistics. An important part of data analysis is validating your assumptions with real-world data to see if there is a pattern or a particular user behavior that you can validate. This book will help you get up to speed with the basics of data analysis and approaches in the Haskell language.
Put your Haskell skills to work and generate publication-ready visualizations in no time at all
Key Features
Take your data analysis skills to the next level using the power of Haskell
Understand regression analysis, perform multivariate regression, and untangle different cluster varieties
Create publication-ready visualizations of data
Book DescriptionEvery business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner.
This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world.
By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.
What you will learn
Learn to parse a CSV file and read data into the Haskell environment
Create Haskell functions for common descriptive statistics functions
Create an SQLite3 database using an existing CSV file
Learn the versatility of SELECT queries for slicing data into smaller chunks
Apply regular expressions in large-scale datasets using both CSV and SQLite3 files
Create a Kernel Density Estimator visualization using normal distribution
Who this book is forThis book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into the functional programming style.
Put your Haskell skills to work and generate publication-ready visualizations in no time at all
Key Features
Take your data analysis skills to the next level using the power of Haskell
Understand regression analysis, perform multivariate regression, and untangle different cluster varieties
Create publication-ready visualizations of data
Book DescriptionEvery business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner.
This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world.
By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.
What you will learn
Learn to parse a CSV file and read data into the Haskell environment
Create Haskell functions for common descriptive statistics functions
Create an SQLite3 database using an existing CSV file
Learn the versatility of SELECT queries for slicing data into smaller chunks
Apply regular expressions in large-scale datasets using both CSV and SQLite3 files
Create a Kernel Density Estimator visualization using normal distribution
Who this book is forThis book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into the functional programming style.
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.
Table of Contents
Descriptive Statistics
SQLite3
Regular Expressions
Visualizations
Kernel Density Estimation
Course Review
| Erscheinungsdatum | 03.11.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 1-78980-286-5 / 1789802865 |
| ISBN-13 | 978-1-78980-286-3 / 9781789802863 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Datenanalyse für Künstliche Intelligenz
Buch | Softcover (2024)
De Gruyter Oldenbourg (Verlag)
CHF 104,90
Auswertung von Daten mit pandas, NumPy und IPython
Buch | Softcover (2023)
O'Reilly (Verlag)
CHF 62,85
