Reproducible Research
date_range Débute le 13 mars 2017
event_note Se termine le 10 avril 2017
list 4 séquences
assignment Niveau : Introductif
label Informatique & Programmation
chat_bubble_outline Langue : Anglais
card_giftcard 9.6 points
4 /5
Avis de la communauté
112 avis

Les infos clés

credit_card Formation gratuite
verified_user Certification gratuite
timer 16 heures de cours

En résumé

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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Le programme

  • 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 ...
  • 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...
  • 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...
  • 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.

Le concepteur

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.

La plateforme

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

Avis de la communauté
4 /5 Moyenne
Le meilleur avis

This course spanned a single but important topic. The assignments were really important and challenging ( I spent several days on the second one). Overall, a fun course but don't expect anything like R Programming or Getting and Cleaning Data in terms of usefulness.

le 2 février 2018
Quelle note donnez-vous à cette ressource ?
le 5 mars 2018

Same information repeated almost all the time ... it looks like the video were made independantly of the course and simply uploaded into Coursera as is .. It is ok in general but in this case, it was really painful to watch. Like video 1 (5min) then video 2 (6 min with 3 as a reminder of the video 1). Reminder are fine across courses or even in different weeks of the same course but not in 2 videos in a row. Otherwise content interesting but could have been explained in way less time.

le 3 mars 2018

Very helpful and informative information on how to create reproducible research. The project gives you an opportunity to create reproducible research in the format of a report.

le 23 février 2018

Most of the knowledge one needs can be perceived till week 2 only. Week 3 is a complete repetition of previous 2 weeks. While week 4 offers case studies which I feel are not much important. But overall the experience was good.

le 22 février 2018

This is a necessary evil. You can try to do the other classes in the specialization without it, but learning to use R markdown well is hard with out this or a similar class

le 5 février 2018

A few of the lectures were a bit repetitive if you are taking the full data science specialization. Overall there are some valuable skills and thought patterns that will prove useful if interested in reproducibility and clarity of analysis.

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