Graduate-level course exploring the dynamic relationship between biomedical data and applications Read more

Course Overview

This course is a comprehensive introduction to the fundamental concepts of data wrangling and data quality, focusing on their importance in the context of biomedical research and applications. Students gain both theoretical knowledge and practical skills to manage, clean, and transform data effectively. The course emphasizes real-world applications, utilizing data from diverse biomedical domains and sources to illustrate best practices and common challenges in ensuring data accuracy, consistency, and usability.

Key Learning Objectives

• Understand the foundational principles of data wrangling and data quality in biomedical contexts.
• Explore the lifecycle of biomedical data, including extraction, transformation, integration, assimilation, and consumption.
• Develop the ability to critically assess and communicate the quality of data across different stages of its lifecycle.
• Gain hands-on experience with tools and techniques for managing and analyzing biomedical data.

Practical Aplications

Students will engage in practical exercises and case studies to apply concepts in real-world biomedical scenarios. This includes:

• Designing and implementing data engineering pipelines to support research and operations.
• Evaluating data quality in various biomedical domains, addressing challenges such as missing data, inconsistencies, and integration issues.
• Learning to leverage data wrangling techniques to prepare datasets for downstream biomedical analyses and research.