About this course: 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.

Created by: Johns Hopkins University

Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health

Basic Info	Course 5 of 10 in the Data Science Specialization.
Commitment	4-9 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.4 stars Average User Rating 4.4See what learners said

Syllabus

WEEK 1

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 ...

9 videos, 3 readings

Graded: Week 1 Quiz

WEEK 2

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 wi...

9 videos

Graded: Week 2 Quiz

Graded: Course Project 1

WEEK 3

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 an...

10 videos

WEEK 4

Week 4: Case Studies & Commentaries

This week there are two case studies involving the importance of reproducibility in science for you to watch.

5 videos, 1 reading

Graded: Course Project 2

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Creators

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Pricing

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Ratings and Reviews

Rated 4.4 out of 5 of 1,589 ratings

Excellent course!

Great opportunity to become more scientific report the job in R

should be included much more in course 1. it would have been great to know up front how easy it is to mix text and code, not from a reproducibility standpoint, but just to take notes.

Learning Knitr was cool. However, many of the slides were not directly relevant to the course. I think, more rigor can be added, or this course can be merged with one of the others.

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