Data Cleaning and Wrangling Guide

This book addresses the costly issue of dirty data in businesses. This book provides a comprehensive method for cleaning and classifying data, covering essential topics such as data classification, normalization, and taxonomies. It introduces the COAT methodology, which ensures data is Consistent, Organized, Accurate, and Trustworthy (COAT). With practical examples and data horror stories, readers will develop the skills and confidence to manage data effectively and understand its broader impact on an organization. Written in an engaging and practical style, this book is suitable for readers of all experience levels.

This book is a practical guide to mastering data cleaning techniques essential for data science, analysis, and machine learning. It provides insights and heuristics that experienced data scientists have learned through experience. Through a mix of Python, R, and command-line tools, the book covers the entire data cleaning pipeline, from data ingestion to feature engineering, with a focus on understanding the principles behind each step. Detailed exercises at the end of each chapter help solidify your skills, making it a valuable resource for both professionals and students. Ideal for software developers, data scientists, and educators, the book assumes basic knowledge of statistics, programming, and data science concepts.

This book provides a comprehensive overview of the data cleaning process, crucial for ensuring data quality, which directly impacts the accuracy of data analytics and business decisions. It covers four key data cleaning tasks: outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Additionally, it explores the integration of machine learning techniques in data cleaning and how clean data enhances machine learning models. Aimed at researchers, practitioners, and graduate students, the book also highlights future research directions in this evolving field.

This book addresses the critical challenge of maintaining high data quality in data warehouses, essential for accurate business reporting and decision-making. It begins by clarifying the often vague goals of data cleaning and abstracts common data cleaning tasks to develop effective solutions. The book emphasizes an operator-centric approach, where customizable operators serve as building blocks for developing data cleaning solutions, similar to relational algebra in query processing. It also covers the creation of custom scripts that combine these operators with relational ones to implement efficient data cleaning strategies.

This book serves as a comprehensive guide to automated statistical data cleaning, focusing on methods for cleaning textual, numeric, and categorical data. It emphasizes statistical data validation, data cleaning based on predefined restrictions, and the overall data cleaning strategy. Key features include a focus on automation using R, guidance for designing data cleaning processes for both one-time analysis and regular production systems, and techniques for addressing issues like incompleteness, contradictions, and outliers. The book is supported by an accompanying website with data and R code, making it a valuable resource for data scientists, statistical analysts, and educators.

This book, authored and edited by leading experts in the field, provides a comprehensive overview of data wrangling, covering state-of-the-art research and the latest technological advancements. Data wrangling involves cleaning and unifying messy and complex datasets to make them more accessible and analyzable, often transforming raw data into a more usable format. The book distinguishes between data cleaning, which removes inaccuracies, and data wrangling, which focuses on reformatting data. It also highlights various tools and applications designed to handle complex data at scale, such as Datameer, Infogix, and Trifacta. This volume is essential for professionals looking to master agile analytic processes in data-driven enterprises, making it an invaluable resource for anyone aiming to tackle complex data challenges in their work.

This book is designed for aspiring data scientists and anyone involved in data cleaning tasks. It covers essential features of NumPy, Pandas, and MySQL, with a focus on data wrangling using Python and awk-based shell scripts. The book also introduces basic Python 3, Java, and Pandas programming, along with RDBMS and SQL concepts. Companion files with code are available for download from the publisher, making it a practical resource for hands-on learning.

This book is a hands-on guide that teaches you how to build a JavaScript-based data processing pipeline, handle various types of data, and develop effective troubleshooting strategies. It covers everything from data acquisition and cleaning to creating interactive visualizations with D3 and deploying your applications to production. Designed for intermediate JavaScript developers, this book does not require prior data analysis experience. It equips you with a reusable toolkit for data wrangling, allowing you to manage the entire data analysis process within JavaScript, from start to finish. The print edition includes a free eBook in multiple formats from Manning Publications.

This book takes your data analysis skills beyond spreadsheet tools by teaching you just enough Python to accomplish tasks efficiently. Designed for non-programmers, it guides you through handling data that’s messy or difficult to access, with no prior Python knowledge required. Through step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data effectively. The book also covers automating data processes, managing larger datasets, and creating compelling data stories. You’ll gain a solid understanding of Python syntax, data types, web scraping, data cleaning, and using Python libraries to streamline your data-wrangling tasks.

This guide is designed for statisticians, data scientists, and R users, focusing on the essentials of data preprocessing using the R programming language. It covers the critical process of data wrangling—also known as data munging or manipulation—which is essential for turning noisy data into usable information. Given that data cleaning and preparation consume around 80% of the data analysis workflow, this book aims to make users fluent and efficient in these techniques. Through step-by-step tutorials, readers will learn how to work with various data types, understand and manipulate data structures, acquire and parse inaccessible data, develop functions, use loop control structures, and simplify code with pipe operators. By the end of the book, users will be well-equipped to reshape, manipulate, summarize, and join data sets, allowing them to focus more on understanding the content of their data.