May 1, 2024
Updated May 11, 2025
23 minute read
Data wrangling, also known as data munging or data remediation, is the comprehensive process of taking raw data and transforming it into a more usable, structured, and reliable format. This crucial step occurs before any in-depth analysis can take place, ensuring that the insights derived are built upon a solid foundation of high-quality data. Think of it as preparing your ingredients before cooking a complex meal; without properly cleaned and organized components, the final dish (your analysis) is unlikely to be successful.
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Reading list
We've selected 29 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Wrangling.
Is considered a fundamental text for anyone wanting to perform data wrangling using Python. Written by the creator of the pandas library, it provides a comprehensive guide to the essential tools for data manipulation, processing, cleaning, and crunching in Python. It is widely used as a reference and often recommended for introductory data analysis courses. The third edition is updated for newer versions of the libraries.
An indispensable resource for data wrangling using the R programming language, this book focuses on the 'tidyverse' collection of packages, which are designed for efficient and elegant data manipulation and visualization. It's a widely adopted textbook in academic settings and a go-to guide for R users in industry. It provides a strong foundation in the principles of tidy data and data transformation.
Delves specifically into the critical aspect of data cleaning within data wrangling. It provides a comprehensive overview of techniques for identifying and repairing errors in data. It's a more specialized text that is highly relevant for those who need to develop a deep understanding of data quality issues and their resolution.
Provides a practical, hands-on approach to data wrangling using Python, aimed at readers who may not have extensive programming backgrounds. It covers essential techniques for acquiring, cleaning, analyzing, and presenting data efficiently. It's a good resource for those looking to move beyond spreadsheet software for data analysis and automate their data processes.
Provides a comprehensive overview of data wrangling with Python, including data cleaning, transformation, and preparation. It is written by Wes McKinney, the creator of the popular Pandas library, which is widely used for data wrangling in Python.
Offers a thorough guide to data cleaning, a fundamental part of data wrangling. It provides step-by-step explanations and best practices for preparing data for analysis, focusing on reducing errors and improving data quality. It's a valuable resource for anyone involved in primary data collection and preparation.
This cookbook provides practical recipes and techniques for cleaning data using Python libraries, particularly pandas. It's a hands-on resource for addressing common data cleaning challenges with code examples. It's well-suited for those who want to quickly find solutions to specific data cleaning problems in Python.
Feature engineering crucial part of data wrangling for machine learning applications. provides a practical guide to creating and transforming features from raw data, which is essential for building effective machine learning models. It's particularly relevant for those interested in the intersection of data wrangling and machine learning.
This practical guide focuses on data preprocessing techniques using Python libraries like pandas and NumPy. It offers hands-on examples and exercises to help solidify understanding of common data preparation tasks. It's a good resource for learners who prefer a practical, code-focused approach to data wrangling in Python.
Offers a practical approach to data wrangling specifically using SQL. It covers essential SQL features for data manipulation, cleaning, and transformation with hands-on examples. It's an excellent resource for those who primarily work with data in relational databases and want to enhance their SQL-based wrangling skills.
Focusing specifically on using SQL for data wrangling and analysis within relational databases, this textbook integrates SQL concepts with the data life cycle. It emphasizes data loading, cleaning, and pre-processing using SQL, which critical skill for working with structured data. It's suitable for those who need to leverage their SQL knowledge for data science tasks.
Covers the basics of data wrangling in R. It introduces the tidyverse, a collection of packages for data science in R, and shows how to use it to clean, transform, and visualize data.
Dieses Buch bietet einen umfassenden Überblick über die Datenaufbereitung mit Python. Es deckt Themen wie Datenbereinigung, -transformation und -aufbereitung ab.
Provides a comprehensive overview of the data engineering lifecycle, which includes a significant focus on data ingestion, transformation, and serving. It's an excellent resource for understanding how data wrangling fits into the broader data engineering landscape and the principles behind building robust data systems.
While covering broader data science concepts, this book offers practical guidance on data manipulation and preparation using R in a business context. It provides a practitioner's perspective on the data science process, including data cleaning and management. It's a valuable resource for those looking to apply data wrangling skills to real-world business problems.
Teaches the fundamentals of data manipulation in SQL. It covers topics such as data cleaning, transformation, and aggregation, and shows how to use SQL to prepare data for analysis.
While not solely focused on data wrangling, this book is highly relevant for professionals who need to build and manage data pipelines that include wrangling steps. Airflow popular tool for orchestrating data workflows, and this book provides guidance on building robust and scalable data processing pipelines. It's valuable for those moving into a data engineering role.
For those dealing with big data, Apache Spark powerful processing engine that is often used for large-scale data wrangling. comprehensive guide to using Spark for various data processing tasks, including transformations and cleaning on distributed datasets. It's essential for anyone working with big data technologies.
Covers data wrangling with MongoDB. It introduces the MongoDB database and shows how to use it to clean, transform, and prepare data for analysis.
Introduces data wrangling with Apache Spark. It covers topics such as data loading, data cleaning, and data transformation, and shows how to use Spark to process large datasets efficiently.
Deep dive into the systems and concepts behind data processing and storage. While not a direct data wrangling how-to, it provides essential background knowledge on how data systems work, which is crucial for understanding the challenges and considerations in large-scale data wrangling. It's a valuable resource for advanced learners and professionals.
Introduces the concept of Data Mesh, a decentralized data architecture that impacts how data is owned, shared, and governed within an organization. Understanding Data Mesh can provide valuable context for data wrangling in large, distributed data environments and highlights contemporary challenges and approaches to data management.
Covers data wrangling with Hadoop. It introduces the Hadoop ecosystem and shows how to use tools such as Pig, Hive, and Sqoop to clean, transform, and prepare data for analysis.
While not strictly a data wrangling book, this classic text on SQL highlights common mistakes and bad practices when working with databases. Understanding these antipatterns is crucial for writing efficient and maintainable SQL code for data wrangling tasks, helping to avoid common pitfalls and improve data quality.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/c54vdz/data
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