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Johns Hopkins University

Reproducible Research

Johns Hopkins University via Coursera


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.


  • Week 1: Concepts, Ideas, & Structure
    • This week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.
  • Week 2: Markdown & knitr
    • This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.
  • Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
    • This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.
  • Week 4: Case Studies & Commentaries
    • This week there are two case studies involving the importance of reproducibility in science for you to watch.

Taught by

Roger Peng


3.9 rating, based on 27 Class Central reviews

4.6 rating at Coursera based on 4154 ratings

Start your review of Reproducible Research

  • Life Is Study
    The first 2.5 weeks of lecture material is great. It provides a well-organized overview of how to create reproducible research in R using R markdown and the knitr package, taking plenty of time to talk about best practices. Thankfully, Roger Peng ha…
  • Brandt Pence
    Reproducible Research is the fifth course in the Data Science specialization, and the last course in what could reasonably be considered the basic R introduction portion of the series. Following this course, students move into Statistical Inference,…
  • Anonymous
    Not much content. Only introduced and taught one main topic: knitr package in R. Much of course spent repetitively advocating for reproducible research with case studies and peer reviewed assignments. Second peer reviewed assignment was essentially the same as the first in terms of learning new techniques. Most of my time completing the course (I spent on average 6 hours per week) was in trying to clean and organize the data, and getting unfamiliar R techniques to work.
    The instructor's personal preference for knitr over Sweave may be contrary to most statistician's preferences.
  • Michal
    The course is a part of very good 'data science with R' program (don't know current name cause it changes) available at Coursera.

    The program is quite massive, it contains about 8 courses but is really thorough and well presented. It is designed with even complete beginners in mind, so may start it without any prior knowledge.
  • The course was solid, and gave a good overview on the why and how of making research reproducible. There's an overemphasis on doing work in R Markdown, but the concepts are generally applicable. Background knowledge in using R and running basic stats is necessary, as the course assumes you already have that going in.
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