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Reproducible Research
  • Level Foundation
  • Duration 8 hours
  • Course by Johns Hopkins University
  • Offered by

About

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.

Modules

Week 1

    • 9 Videos
    • 4 Readings
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9 Videos

  • 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

4 Readings

  • A Note of Explanation
  • Syllabus
  • Pre-course survey
  • Course Book: Report Writing for Data Science in R

Week 1 Quiz

    • 1 Assignment
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1 Assignment

  • Week 1 Quiz

Week 2

    • 8 Videos
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8 Videos

  • Coding Standards in R
  • Markdown
  • R Markdown
  • R Markdown Demonstration
  • knitr (part 1)
  • knitr (part 2)
  • knitr (part 3)
  • knitr (part 4)

Week 2 Quiz

    • 1 Assignment
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1 Assignment

  • Week 2 Quiz

Course Project 1

    • 1 Videos
    • 1 PeerReview
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1 Peer Review

  • Course Project 1

1 Videos

  • Introduction to Course Project 1

Week 3

    • 10 Videos
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10 Videos

  • 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

    • 4 Videos
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4 Videos

  • Caching Computations
  • Case Study: Air Pollution
  • Case Study: High Throughput Biology
  • Commentaries on Data Analysis

Course Project 2

    • 1 Videos
    • 1 PeerReview
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1 Peer Review

  • Course Project 2

1 Videos

  • Introduction to Peer Assessment 2

Share Your Feedback

    • 1 Readings
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1 Readings

  • Post-Course Survey

Auto Summary

The "Reproducible Research" course, offered by Coursera, is an essential foundation-level program within the Data Science & AI domain. It delves into the crucial concepts and tools necessary for reporting modern data analyses in a reproducible way. Emphasizing the growing importance of reproducibility amidst increasingly complex data analyses, the course teaches learners to publish their data and software code alongside their scientific claims. This transparency ensures that others can verify findings and build upon them, focusing on the core content rather than superficial details. Throughout the course, participants will explore literate statistical analysis tools, enabling them to create single documents that seamlessly integrate data analyses. These documents allow others to easily replicate the analysis and achieve the same results, enhancing the utility and credibility of the work. Spanning 480 minutes, this course fits into Coursera's Starter subscription option, making it accessible to those beginning their journey in data science and looking to establish strong, reproducible research practices. Ideal for data enthusiasts and professionals alike, this course empowers learners to contribute more reliably and effectively to the scientific community.

Roger D. Peng, PhD
Instructors

Roger D. Peng, PhD

Jeff Leek, PhD
Instructors

Jeff Leek, PhD

Brian Caffo, PhD
Instructors

Brian Caffo, PhD