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.
Ce cours fait partie de la Spécialisation Data Science
Reproducible Research
proposé par
À propos de ce cours
4.5
2,880 notes
Ce que vous allez apprendre
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
Compétences que vous acquerrez
KnitrData AnalysisR ProgrammingMarkup Language
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Week 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 lectures, 1 quiz
9 vidéos
Introduction1 min
What is Reproducible Research About?8 min
Reproducible Research: Concepts and Ideas (part 1)7 min
Reproducible Research: Concepts and Ideas (part 2) 5 min
Reproducible Research: Concepts and Ideas (part 3) 3 min
Scripting Your Analysis 4 min
Structure of a Data Analysis (part 1)12 min
Structure of a Data Analysis (part 2)17 min
Organizing Your Analysis11 min
3 lectures
Syllabus10 min
Pre-course survey10 min
Course Book: Report Writing for Data Science in R10 min
1 exercice pour s'entraîner
Week 1 Quiz20 min
Semaine
2Week 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 quiz
9 vidéos
Markdown5 min
R Markdown6 min
R Markdown Demonstration7 min
knitr (part 1)7 min
knitr (part 2) 4 min
knitr (part 3) 4 min
knitr (part 4) 9 min
Introduction to Course Project 14 min
1 exercice pour s'entraîner
Week 2 Quiz10 min
Semaine
3Week 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 vidéos
RPubs 3 min
Reproducible Research Checklist (part 1)8 min
Reproducible Research Checklist (part 2) 10 min
Reproducible Research Checklist (part 3) 6 min
Evidence-based Data Analysis (part 1)3 min
Evidence-based Data Analysis (part 2) 3 min
Evidence-based Data Analysis (part 3) 4 min
Evidence-based Data Analysis (part 4) 4 min
Evidence-based Data Analysis (part 5) 7 min
Semaine
4Week 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 lecture, 1 quiz
5 vidéos
Caching Computations11 min
Case Study: Air Pollution14 min
Case Study: High Throughput Biology30 min
Commentaries on Data Analysis2 min
Introduction to Peer Assessment 2 min
1 lecture
Post-Course Survey10 min
4.5
428 avis35%
a commencé une nouvelle carrière après avoir terminé ces cours
83%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
par AA•Feb 13th 2016
My favorite course, at least it gives me an argument why scripted statistics is awesome and can be applied to a number of data related activities. Recycling chunks of code has proven useful to me.
par AS•Jun 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.
Enseignants
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
À propos de 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.
À propos de la Spécialisation Data Science
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Foire Aux Questions
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Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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