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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Университет Джонса ХопкинсаОб этом курсе
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Попробуйте Coursera для бизнесаЧему вы научитесь
Organize data analysis to help make it more reproducible
Write up a reproducible data analysis using knitr
Determine the reproducibility of analysis project
Publish reproducible web documents using Markdown
Приобретаемые навыки
- Knitr
- Data Analysis
- R Programming
- Markup Language
Будет ли вашей компании выгодно обучить сотрудников востребованным навыкам?
Попробуйте Coursera для бизнеса
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Программа курса: что вы изучите
Week 1: Concepts, Ideas, & Structure
Week 2: Markdown & knitr
Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
Week 4: Case Studies & Commentaries
Рецензии
- 5 stars68,61 %
- 4 stars22,96 %
- 3 stars5,73 %
- 2 stars1,64 %
- 1 star1,04 %
Лучшие отзывы о курсе ВОСПРОИЗВОДИМОЕ ИССЛЕДОВАНИЕ
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
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.
A very important course that greatly improved my ability to communicate the findings of any sort of data analysis that I perform. This is a critical skill to acquire to "deliver the means."
I took this course as part of the Data Science specialization without any real expectation and realized that this subject is probably one of the most important in data analysis.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
Остались вопросы? Посетите Центр поддержки учащихся.
