This Specialization is intended for scientific researchers who work with data and want to make their analyses yield consistent results regardless of who conducts the analysis or when it is run. The four topic courses and capstone course will teach you best practices, help you practice hands-on skills, and provide templates to help you adapt the content for your own research needs. Students will learn about code review, version control with Git and GitHub, using containers with tools like Docker to keep computing environments consistent, and using continuous integration/deployment tools like GitHub Actions to automatically run and test your code.

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Enhancing Reproducible Science with GitHub and Docker Specialization
Make Your Scientific Data Analysis Reproducible. Learn best practices, methods, and tools to enhance the reproducibility of your data analyses



Instructors: Carrie Wright, PhD
Included with
Recommended experience
Recommended experience
What you'll learn
Organize analysis files and track changes in your code over time with version control.
Automate the testing and re-running of your code.
Enable shareable computing environments so that your results are consistent.
Overview
Skills you'll gain
- Code Review
- Package and Software Management
- Continuous Integration
- Development Environment
- Application Deployment
- Software Documentation
- Key Management
- Version Control
- Scientific Methods
- Containerization
- Open Source Technology
- Continuous Deployment
- Automation
- Software Development Tools
- CI/CD
- Data Sharing
- Technical Documentation
Tools you'll learn
What’s included

Add to your LinkedIn profile
July 2025
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Fred Hutchinson Cancer Center

Specialization - 5 course series
What you'll learn
Create reproducible data analyses
Apply reproducibility skills to existing analyses scripts and projects
Skills you'll gain
What you'll learn
Enhance reproducibility and replicability of data analyses
Introduction to reproducibility tools
Skills you'll gain
What you'll learn
Goal of this course: Equip learners with basics skills and confidence to utilize containers within the context of scientific software analyses.
Skills you'll gain
What you'll learn
Utilize automation to enhance scientific projects and save time
Troubleshoot most common GitHub Actions errors
Skills you'll gain
What you'll learn
Skills you'll gain
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructors



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Frequently asked questions
Some familiarity with programming or writing code is required.
Yes, it is recommended that you take the courses in the order suggested.
No, but you can earn a certificate if you do a paid enrollment in the course and complete all activities.
More questions
Financial aid available,