RED 765 | OpenRefine/Data Wrangling

This is a REd aynchronous (self-paced) class.

Class Description: This course introduces data cleaning, wrangling, and exploration using OpenRefine, a powerful open-source tool for working with messy data. Participants will learn essential techniques for transforming, correcting, and organizing datasets to ensure data quality and reliability by exploring the interface and key features of OpenRefine, including General Refine Expression Language (GREL) transformations, facets, filters, and clustering techniques. Through hands-on exercises, attendees will gain practical experience in handling common data issues and preparing data for analysis. We’ll also cover how OpenRefine can be used to gain key insights and perform basic qualitative coding. This course is open to all; no experience with coding languages, data cleaning, or data analytics required. 

Learning Outcomes: By the end of the class, participants will be able to:

• Discuss the fundamentals of data cleaning and wrangling and the importance of data quality
• Navigate OpenRefine’s interface and perform basic GREL functions for transforming data in OpenRefine
• Apply various transformation techniques to standardize and normalize data values effectively and efficiently
• Detect and correct common data errors including inconsistencies, duplicates, and missing values
• Prepare datasets for further analysis and visualization in other tools and software

Instructor:

Kaylee P. Alexander, PhD
Assistant Research Data Librarian
kaylee.alexander@utah.edu
Data Management, J. Willard Marriott Library 

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REd Asynchronous classes feature lessons and exercises designed to build competency and increase efficiency. Modules are accessible 24/7 and are all self-paced. All members of the University research community are invited to complete any online classes of interest.