Reproducible Research

  • 4.6
Approx. 8 hours to complete

Course Summary

Learn how to create reproducible research projects using R Markdown, Git, and GitHub. This course will teach you how to organize, document, and share your research in a way that promotes transparency and makes it easy for others to reproduce your results.

Key Learning Points

  • Understand the importance of reproducibility in research
  • Learn how to use R Markdown to create reproducible reports
  • Learn how to use Git and GitHub for version control and collaboration

Related Topics for further study


Learning Outcomes

  • Create reproducible research reports using R Markdown
  • Use Git and GitHub for version control and collaboration
  • Understand the importance of reproducibility in research

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of R programming
  • Familiarity with Git and GitHub is helpful but not required

Course Difficulty Level

Intermediate

Course Format

  • Online self-paced course
  • Video lectures
  • Hands-on exercises

Similar Courses

  • Data Science: Reproducible Workflow
  • Data Science: Collaboration and Reproducibility

Related Education Paths


Notable People in This Field

  • Hadley Wickham
  • Karl Broman

Related Books

Description

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

Knowledge

  • Organize data analysis to help make it more reproducible
  • Write up a reproducible data analysis using knitr
  • Determine the reproducibility of analysis project
  • Publish reproducible web documents using Markdown

Outline

  • Week 1: Concepts, Ideas, & Structure
  • Introduction
  • What is Reproducible Research About?
  • Reproducible Research: Concepts and Ideas (part 1)
  • Reproducible Research: Concepts and Ideas (part 2)
  • Reproducible Research: Concepts and Ideas (part 3)
  • Scripting Your Analysis
  • Structure of a Data Analysis (part 1)
  • Structure of a Data Analysis (part 2)
  • Organizing Your Analysis
  • A Note of Explanation
  • Syllabus
  • Pre-course survey
  • Course Book: Report Writing for Data Science in R
  • Week 1 Quiz
  • Week 2: Markdown & knitr
  • Coding Standards in R
  • Markdown
  • R Markdown
  • R Markdown Demonstration
  • knitr (part 1)
  • knitr (part 2)
  • knitr (part 3)
  • knitr (part 4)
  • Introduction to Course Project 1
  • Week 2 Quiz
  • Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
  • Communicating Results
  • RPubs
  • Reproducible Research Checklist (part 1)
  • Reproducible Research Checklist (part 2)
  • Reproducible Research Checklist (part 3)
  • Evidence-based Data Analysis (part 1)
  • Evidence-based Data Analysis (part 2)
  • Evidence-based Data Analysis (part 3)
  • Evidence-based Data Analysis (part 4)
  • Evidence-based Data Analysis (part 5)
  • Week 4: Case Studies & Commentaries
  • Caching Computations
  • Case Study: Air Pollution
  • Case Study: High Throughput Biology
  • Commentaries on Data Analysis
  • Introduction to Peer Assessment 2
  • Post-Course Survey

Summary of User Reviews

Discover the importance of reproducibility in research with this course on Reproducible Research. Students have consistently praised this course for its comprehensive coverage and engaging lectures, resulting in a high overall rating.

Key Aspect Users Liked About This Course

Many users have appreciated the interactive assignments and practical examples that are provided throughout the course.

Pros from User Reviews

  • Comprehensive course content
  • Engaging lectures and interactive assignments
  • Practical examples
  • In-depth coverage of reproducibility in research
  • Great for beginners and advanced learners

Cons from User Reviews

  • Some users found the lectures to be too long
  • Lack of interaction with course instructors
  • Some users found the assignments to be too time-consuming
  • Not enough emphasis on statistics and data analysis
  • Some users found the course to be too basic
English
Available now
Approx. 8 hours to complete
Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Johns Hopkins University
Coursera

Instructor

Roger D. Peng, PhD

  • 4.6 Raiting
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