Getting Started with Haskell Data Analysis PDF Download
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Author: James Church
Publisher: Packt Publishing Ltd
ISBN: 178980860X
Category : Computers
Languages : en
Pages : 156
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Book Description
Put your Haskell skills to work and generate publication-ready visualizations in no time at all Key FeaturesTake your data analysis skills to the next level using the power of HaskellUnderstand regression analysis, perform multivariate regression, and untangle different cluster varietiesCreate publication-ready visualizations of dataBook Description Every 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 learnLearn to parse a CSV file and read data into the Haskell environmentCreate Haskell functions for common descriptive statistics functionsCreate an SQLite3 database using an existing CSV fileLearn the versatility of SELECT queries for slicing data into smaller chunksApply regular expressions in large-scale datasets using both CSV and SQLite3 filesCreate a Kernel Density Estimator visualization using normal distributionWho this book is for This 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.
Author: James Church
Publisher: Packt Publishing Ltd
ISBN: 178980860X
Category : Computers
Languages : en
Pages : 156
Get Book Here
Book Description
Put your Haskell skills to work and generate publication-ready visualizations in no time at all Key FeaturesTake your data analysis skills to the next level using the power of HaskellUnderstand regression analysis, perform multivariate regression, and untangle different cluster varietiesCreate publication-ready visualizations of dataBook Description Every 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 learnLearn to parse a CSV file and read data into the Haskell environmentCreate Haskell functions for common descriptive statistics functionsCreate an SQLite3 database using an existing CSV fileLearn the versatility of SELECT queries for slicing data into smaller chunksApply regular expressions in large-scale datasets using both CSV and SQLite3 filesCreate a Kernel Density Estimator visualization using normal distributionWho this book is for This 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.
Author: James Church
Publisher: Packt Publishing
ISBN: 9781784394707
Category : Computers
Languages : en
Pages : 198
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Book Description
If you are a developer, analyst, or data scientist who wants to learn data analysis methods using Haskell and its libraries, then this book is for you. Prior experience with Haskell and a basic knowledge of data science will be beneficial.
Author: Nishant Shukla
Publisher: Packt Publishing Ltd
ISBN: 1783286342
Category : Computers
Languages : en
Pages : 573
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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.
Author: Will Kurt
Publisher: Simon and Schuster
ISBN: 1638356777
Category : Computers
Languages : en
Pages : 824
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Book Description
Summary Get Programming with Haskell leads you through short lessons, examples, and exercises designed to make Haskell your own. It has crystal-clear illustrations and guided practice. You will write and test dozens of interesting programs and dive into custom Haskell modules. You will gain a new perspective on programming plus the practical ability to use Haskell in the everyday world. (The 80 IQ points: not guaranteed.) Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Programming languages often differ only around the edges—a few keywords, libraries, or platform choices. Haskell gives you an entirely new point of view. To the software pioneer Alan Kay, a change in perspective can be worth 80 IQ points and Haskellers agree on the dramatic benefits of thinking the Haskell way—thinking functionally, with type safety, mathematical certainty, and more. In this hands-on book, that's exactly what you'll learn to do. What's Inside Thinking in Haskell Functional programming basics Programming in types Real-world applications for Haskell About the Reader Written for readers who know one or more programming languages. Table of Contents Lesson 1 Getting started with Haskell Unit 1 - FOUNDATIONS OF FUNCTIONAL PROGRAMMING Lesson 2 Functions and functional programming Lesson 3 Lambda functions and lexical scope Lesson 4 First-class functions Lesson 5 Closures and partial application Lesson 6 Lists Lesson 7 Rules for recursion and pattern matching Lesson 8 Writing recursive functions Lesson 9 Higher-order functions Lesson 10 Capstone: Functional object-oriented programming with robots! Unit 2 - INTRODUCING TYPES Lesson 11 Type basics Lesson 12 Creating your own types Lesson 13 Type classes Lesson 14 Using type classes Lesson 15 Capstone: Secret messages! Unit 3 - PROGRAMMING IN TYPES Lesson 16 Creating types with "and" and "or" Lesson 17 Design by composition—Semigroups and Monoids Lesson 18 Parameterized types Lesson 19 The Maybe type: dealing with missing values Lesson 20 Capstone: Time series Unit 4 - IO IN HASKELL Lesson 21 Hello World!—introducing IO types Lesson 22 Interacting with the command line and lazy I/O Lesson 23 Working with text and Unicode Lesson 24 Working with files Lesson 25 Working with binary data Lesson 26 Capstone: Processing binary files and book data Unit 5 - WORKING WITH TYPE IN A CONTEXT Lesson 27 The Functor type class Lesson 28 A peek at the Applicative type class: using functions in a context Lesson 29 Lists as context: a deeper look at the Applicative type class Lesson 30 Introducing the Monad type class Lesson 31 Making Monads easier with donotation Lesson 32 The list monad and list comprehensions Lesson 33 Capstone: SQL-like queries in Haskell Unit 6 - ORGANIZING CODE AND BUILDING PROJECTS Lesson 34 Organizing Haskell code with modules Lesson 35 Building projects with stack Lesson 36 Property testing with QuickCheck Lesson 37 Capstone: Building a prime-number library Unit 7 - PRACTICAL HASKELL Lesson 38 Errors in Haskell and the Either type Lesson 39 Making HTTP requests in Haskell Lesson 40 Working with JSON data by using Aeson Lesson 41 Using databases in Haskell Lesson 42 Efficient, stateful arrays in Haskell Afterword - What's next? Appendix - Sample answers to exercise
Author: Bryan O'Sullivan
Publisher: "O'Reilly Media, Inc."
ISBN: 0596554303
Category : Computers
Languages : en
Pages : 714
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Book Description
This easy-to-use, fast-moving tutorial introduces you to functional programming with Haskell. You'll learn how to use Haskell in a variety of practical ways, from short scripts to large and demanding applications. Real World Haskell takes you through the basics of functional programming at a brisk pace, and then helps you increase your understanding of Haskell in real-world issues like I/O, performance, dealing with data, concurrency, and more as you move through each chapter.
Author: Phuong Vo.T.H
Publisher: Packt Publishing Ltd
ISBN: 1783988452
Category : Computers
Languages : en
Pages : 188
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Book Description
Learn to use powerful Python libraries for effective data processing and analysis About This Book Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib Create, manipulate, and analyze your data to extract useful information to optimize your system A hands-on guide to help you learn data analysis using Python Who This Book Is For If you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you. What You Will Learn Understand the importance of data analysis and get familiar with its processing steps Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis Create effective visualizations to present your data using Matplotlib Process and analyze data using the time series capabilities of Pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply the supported Python package to data analysis applications through examples Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python library In Detail Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis. With this book, we will get you started with Python data analysis and show you what its advantages are. The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems. Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples. Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn. Style and approach This is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
Author: Alejandro Serrano Mena
Publisher: Apress
ISBN: 148424480X
Category : Computers
Languages : en
Pages : 600
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Book Description
Get a practical, hands-on introduction to the Haskell language, its libraries and environment, and to the functional programming paradigm that is fast growing in importance in the software industry. This book contains excellent coverage of the Haskell ecosystem and supporting tools, include Cabal and Stack for managing projects, HUnit and QuickCheck for software testing, the Spock framework for developing web applications, Persistent and Esqueleto for database access, and parallel and distributed programming libraries. You’ll see how functional programming is gathering momentum, allowing you to express yourself in a more concise way, reducing boilerplate, and increasing the safety of your code. Haskell is an elegant and noise-free pure functional language with a long history, having a huge number of library contributors and an active community. This makes Haskell the best tool for both learning and applying functional programming, and Practical Haskell takes advantage of this to show off the language and what it can do. What You Will Learn Get started programming with Haskell Examine the different parts of the language Gain an overview of the most important libraries and tools in the Haskell ecosystem Apply functional patterns in real-world scenarios Understand monads and monad transformers Proficiently use laziness and resource management Who This Book Is For Experienced programmers who may be new to the Haskell programming language. However, some prior exposure to Haskell is recommended.
Author: Christopher Allen
Publisher:
ISBN: 9781945388033
Category :
Languages : en
Pages :
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Book Description
Haskell Programming makes Haskell as clear, painless, and practical as it can be, whether you're a beginner or an experienced hacker. Learning Haskell from the ground up is easier and works better. With our exercise-driven approach, you'll build on previous chapters such that by the time you reach the notorious Monad, it'll seem trivial.
Author: Stefania Loredana Nita
Publisher: Apress
ISBN: 1484245075
Category : Computers
Languages : en
Pages : 213
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Book Description
This condensed code and syntax reference presents the essential Haskell syntax in a well-organized format that can be used as a quick and handy reference, including applications to cloud computing and data analysis. This book covers the functional programming features of Haskell as well as strong static typing, lazy evaluation, extensive parallelism, and concurrency You won’t find any technical jargon, bloated samples, drawn out history lessons, or witty stories in this book. What you will find is a language reference that is concise, to the point and highly accessible. The Haskell Quick Syntax Reference is packed with useful information and is a must-have for any Haskell programmer working in big data, data science, and cloud computing. What You Will Learn Quickly and effectively use the Haskell programming language Take advantage of strong static typing Work with lazy evaluations Harness concurrency and extensive parallelism using Haskell Who This Book Is For Experienced programmers who may be new to Haskell or have experience with Haskell and who just want a quick reference guide on it.
Author: Rafael A. Irizarry
Publisher: CRC Press
ISBN: 1000708039
Category : Mathematics
Languages : en
Pages : 836
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Book Description
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
