Learn essential concepts and tools for understanding genomic data analysis, perfect for biomedical researchers and students.
Learn essential concepts and tools for understanding genomic data analysis, perfect for biomedical researchers and students.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Researcher's Guide to Omic Data Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
What you'll learn
Understand and interpret different types of genomic data
Choose appropriate tools for omic data analysis
Manage and utilize metadata effectively
Navigate sequencing and microarray data basics
Apply annotation tools and general analysis methods
Skills you'll gain
This course includes:
0.22 Hours PreRecorded video
15 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This foundational course provides biomedical researchers with essential knowledge for understanding and interpreting genomic data. Students learn about different omic data types, workflows, and analysis tools, with a focus on practical applications. The curriculum covers sequencing data fundamentals, microarray analysis, metadata management, and genome annotation, preparing researchers to effectively use bioinformatics resources and communicate with experts.
Introduction
Module 1 · 41 Minutes to complete
Considerations for omic projects
Module 2 · 1 Hours to complete
Omic Basics
Module 3 · 57 Minutes to complete
Annotation and general tools
Module 4 · 51 Minutes to complete
Wrapping Up
Module 5 · 51 Minutes to complete
Fee Structure
Instructor
Pioneering Accessible Genomic Data Science Education
Candace Savonen has established herself as a dedicated educator and innovator in genomic data science, focusing on making complex bioinformatics tools and concepts accessible to diverse audiences. Her work centers on developing comprehensive educational materials that emphasize reproducibility and scalable teaching methods in bioinformatics and cancer genomics. As a key contributor to various educational initiatives, she has been instrumental in creating and delivering bioinformatics education materials specifically tailored for cancer genomics research. Her approach combines practical application with theoretical understanding, ensuring that learners can effectively apply genomic tools to their specific areas of expertise. Through her involvement in developing online learning modules and educational resources, she has demonstrated a commitment to breaking down barriers in genomic data science education, making these essential tools and knowledge more attainable for researchers and practitioners across different fields. Her teaching methodology emphasizes the importance of reproducible research practices and sustainable approaches to data analysis, contributing significantly to the democratization of genomic data science education.
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Frequently asked questions
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