Reproducible Research

4.5
2,462 ratings
383 reviews

Course 5 of 10 in the Data Science Specialization

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.
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100% 在线课程

立即开始,按照自己的计划学习。
Clock

完成时间大约为10 小时

建议:4-9 hours/week
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字幕:English

您将学到的内容有

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

您将获得的技能

R ProgrammingData AnalysisStatisticsGithub
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Clock

完成时间大约为10 小时

建议:4-9 hours/week
Comment Dots

English

字幕:English

Syllabus - What you will learn from this course

1

Section
Clock
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. ...
Reading
9 videos (Total 72 min), 3 readings, 1 quiz
Video9 videos
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
Reading3 readings
Syllabus10m
Pre-course survey10m
Course Book: Report Writing for Data Science in R10m
Quiz1 practice exercises
Week 1 Quiz20m

2

Section
Clock
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. ...
Reading
9 videos (Total 59 min), 2 quizzes
Video9 videos
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
Quiz1 practice exercises
Week 2 Quiz10m

3

Section
Clock
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....
Reading
10 videos (Total 60 min)
Video10 videos
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
Clock
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....
Reading
5 videos (Total 59 min), 1 reading, 1 quiz
Video5 videos
Case Study: Air Pollution14m
Case Study: High Throughput Biology30m
Commentaries on Data Analysis2m
Introduction to Peer Assessment 20m
Reading1 readings
Post-Course Survey10m
4.5
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started a new career after completing these courses
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83%

got a tangible career benefit from this course

Top Reviews

By ASJun 23rd 2017

Of course, I liked this course. There was even an extra non-graded assignment. Plus two graded assignments. Quality instruction videos and lots of practice. Everything a learner needs.

By JTJul 23rd 2017

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.

Instructors

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Roger D. Peng, PhD

Associate Professor, Biostatistics
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Jeff Leek, PhD

Associate Professor, Biostatistics
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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....

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