Reproducible Research
link 来源:www.coursera.org
list 4个序列
assignment 等级:入门
chat_bubble_outline 语言:英语
language 字幕 : 越南语
card_giftcard 128分
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关于内容

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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课程大纲

  • 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.
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教师

Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health

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内容设计师

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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平台

Coursera

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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4 /5 平均值
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最佳评论

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.

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发布日期2018年2月2日
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2018年3月5日
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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.

2018年3月3日
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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.

2018年2月23日
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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.

2018年2月22日
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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

2018年2月5日
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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.