Wrangle By Test Book
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R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Data Wrangling with Python
Author: Jacqueline Kazil
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-02-04
How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process
Data Wrangling with JavaScript
Summary Data Wrangling with JavaScript is hands-on guide that will teach you how to create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Why not handle your data analysis in JavaScript? Modern libraries and data handling techniques mean you can collect, clean, process, store, visualize, and present web application data while enjoying the efficiency of a single-language pipeline and data-centric web applications that stay in JavaScript end to end. About the Book Data Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you'll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You'll also build interactive visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit. What's inside Establishing a data pipeline Acquisition, storage, and retrieval Handling unusual data sets Cleaning and preparing raw dataInteractive visualizations with D3 About the Reader Written for intermediate JavaScript developers. No data analysis experience required. About the Author Ashley Davis is a software developer, entrepreneur, author, and the creator of Data-Forge and Data-Forge Notebook, software for data transformation, analysis, and visualization in JavaScript. Table of Contents Getting started: establishing your data pipeline Getting started with Node.js Acquisition, storage, and retrieval Working with unusual data Exploratory coding Clean and prepare Dealing with huge data files Working with a mountain of data Practical data analysis Browser-based visualization Server-side visualization Live data Advanced visualization with D3 Getting to production