Cleaning Messy Data With OpenRefine / Sidney Gavel.

Format:

Book

Author/Creator:

Gavel, Sidney, author.

Language:

English

Subjects (All):

Quantitative research.

Physical Description:

1 online resource

Place of Publication:

London : SAGE Publications Ltd, 2025.

Summary:

Data cleaning is a critical step in preparing datasets for analysis, ensuring accuracy and reliability in results and adding to the overall value of your project. Depending on the nature of the data and the specific goals of the analysis, data cleaning can encompass a range of techniques tailored to address various types of data issues. While this means data cleaning will look different from dataset to dataset, key steps typically include removing duplicate data, filtering irrelevant data, addressing structural issues, and handling missing data. Proper documentation throughout this process is also essential for reproducibility and collaboration. Using the Metropolitan Museum of Art Open Access CSV dataset as an example, this text illustrates the application of data cleaning techniques using OpenRefine, an open-source cleaning tool, showcasing how to prepare data for analysis to determine the likelihood of objects being highlighted at the museum based on their type, age, or size. After reviewing this article, readers will be able to describe the importance of data cleaning as a foundation of data work including the typical steps involved and how they can be performed in OpenRefine. The dataset file is accompanied by a teaching guide, a student guide and how-to guide.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-03-621651-9

9781036216511

OCLC:

1523170248