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.

100% 在线课程

立即开始，按照自己的计划学习。

完成时间大约为10 小时

建议：4-9 hours/week

English

字幕：English

您将学到的内容有

Determine the reproducibility of analysis project

Organize data analysis to help make it more reproducible

Publish reproducible web documents using Markdown

Write up a reproducible data analysis using knitr

您将获得的技能

R ProgrammingData AnalysisStatisticsGithub

100% 在线课程

立即开始，按照自己的计划学习。

完成时间大约为10 小时

建议：4-9 hours/week

English

字幕：English

Syllabus - What you will learn from this course

1

Section

2 hours to complete

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. ...

9 videos (Total 72 min), 3 readings, 1 quiz

9 videos

Introduction1m

What is Reproducible Research About?8m

Reproducible Research: Concepts and Ideas (part 1)7m

Reproducible Research: Concepts and Ideas (part 2) 5m

Reproducible Research: Concepts and Ideas (part 3) 3m

Scripting Your Analysis 4m

Structure of a Data Analysis (part 1)12m

Structure of a Data Analysis (part 2)17m

Organizing Your Analysis11m

3 readings

Syllabus10m

Pre-course survey10m

Course Book: Report Writing for Data Science in R10m

1 practice exercises

Week 1 Quiz20m

2

Section

3 hours to complete

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. ...

9 videos (Total 59 min), 2 quizzes

9 videos

Coding Standards in R8m

Markdown5m

R Markdown6m

R Markdown Demonstration7m

knitr (part 1)7m

knitr (part 2) 4m

knitr (part 3) 4m

knitr (part 4) 9m

Introduction to Course Project 14m

1 practice exercises

Week 2 Quiz10m

3

Section

1 hour to complete

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....

10 videos (Total 60 min)

10 videos

Communicating Results6m

RPubs 3m

Reproducible Research Checklist (part 1)8m

Reproducible Research Checklist (part 2) 10m

Reproducible Research Checklist (part 3) 6m

Evidence-based Data Analysis (part 1)3m

Evidence-based Data Analysis (part 2) 3m

Evidence-based Data Analysis (part 3) 4m

Evidence-based Data Analysis (part 4) 4m

Evidence-based Data Analysis (part 5) 7m

4

Section

3 hours to complete

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 (Total 59 min), 1 reading, 1 quiz

5 videos

Caching Computations11m

Case Study: Air Pollution14m

Case Study: High Throughput Biology30m

Commentaries on Data Analysis2m

Introduction to Peer Assessment 20m

1 readings

Post-Course Survey10m

Instructors

Roger D. Peng, PhD

Associate Professor, Biostatistics

Jeff Leek, PhD

Associate Professor, Biostatistics

Brian Caffo, PhD

Professor, Biostatistics

About 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....

Frequently Asked Questions

When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I pay for this course?
If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.
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