Introduction to Reproducibility in Cancer Informatics

Offered byCoursera
Public/Government Institute

Introduction to Reproducibility in Cancer Informatics
at
Coursera
Overview

Duration	7 hours
Total fee	Free
Mode of learning	Online
Official Website	Explore Free Course
Credential	Certificate

Table of content

Overview
Highlights
Course Details
Curriculum

Introduction to Reproducibility in Cancer Informatics
at
Coursera
Highlights

Flexible deadlines in accordance to your schedule.
Earn a Certificate upon completion

Introduction to Reproducibility in Cancer Informatics
at
Coursera
Course details

Skills you will learn

Open source GIT

More about this course

The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research and have not had training in reproducibility tools and methods.
Equipping researchers with the skills to create reproducible data analyses increases the efficiency of everyone involved. Reproducible analyses are more likely to be understood, applied, and replicated by others. This helps expedite the scientific process by helping researchers avoid false positive dead ends. Open source clarity in reproducible methods also saves researchers' time so they don't have to reinvent the proverbial wheel for methods that everyone in the field is already performing.
This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub.
The course includes hands-on exercises for how to apply reproducible code concepts to their code. Individuals who take this course are encouraged to complete these activities as they follow along with the course material to help increase the reproducibility of their analyses.
Equip learners with reproducibility skills they can apply to their existing analyses scripts and projects. This course opts for an "ease into it" approach. We attempt to give learners doable, incremental steps to increase the reproducibility of their analyses.
This course is designed with busy professional learners in mind -- who may have to pick up and put down the course when their schedule allows.