Advanced Reproducibility in Cancer Informatics
Master advanced tools for reproducible cancer informatics research. Learn GitHub, code review, Docker, and automation techniques.
Master advanced tools for reproducible cancer informatics research. Learn GitHub, code review, Docker, and automation techniques.
This course teaches advanced tools to enhance reproducibility in cancer informatics. Learn to use GitHub, conduct code reviews, work with Docker, and implement automation for more reliable and replicable data analyses.
Instructors:
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What you'll learn
Master advanced version control techniques using GitHub
Conduct effective code reviews as both an author and a reviewer
Understand and implement Docker for reproducible research environments
Apply automation tools to enhance the reproducibility of data analyses
Develop strategies for improving the replicability of cancer informatics studies
Gain practical experience with reproducibility tools through hands-on exercises
Skills you'll gain
This course includes:
PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This course provides an advanced exploration of reproducibility tools in cancer informatics. It begins with a definition of reproducibility and its importance in scientific research. The curriculum then delves into version control using GitHub, teaching students how to create branches and pull requests. Extensive coverage is given to the code review process, both from the perspective of an author and a reviewer. The course introduces Docker, guiding students through launching and modifying Docker images. Finally, it explores automation as a tool for enhancing reproducibility. Throughout the course, hands-on exercises allow students to apply these concepts to real-world scenarios in cancer informatics research.
Getting started in this course
Module 1 · 25 Minutes to complete
Defining Reproducibility
Module 2 · 35 Minutes to complete
Version control with GitHub
Module 3 · 2 Hours to complete
Code review - as an author
Module 4 · 1 Hours to complete
Code review -- as a reviewer
Module 5 · 1 Hours to complete
Launching Docker
Module 6 · 1 Hours to complete
Modifying a Docker image
Module 7 · 1 Hours to complete
Automation as a reproducibility tool
Module 8 · 52 Minutes to complete
Fee Structure
Payment options
Financial Aid
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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