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Reproducible Research

Data Science,

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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Rating 4.3 based on 402 ratings
Length 5 weeks
Effort 4-9 hours/week
Starts Oct 5 (3 weeks ago)
Cost $50
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

reproducible research

It provides a well-organized overview of how to create reproducible research in R using R markdown and the knitr package, taking plenty of time to talk about best practices.

Only introduced and taught one main topic: knitr package in R. Much of course spent repetitively advocating for reproducible research with case studies and peer reviewed assignments.

very good 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.

A good informative course to inform about importance of "Reproducible Research", also a good one for practicing code writing and publishing in RPubs and Github.

These are important skills for a data scientist and I'm glad there is a full 4-week course dedicated to reproducible research.

While it may be easy for some to pick this up on their own, I thought Roger's take on it, as well as the other instructors, impressed on the critical nature of Reproducible research.

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data scientist

Key aspect for a good data scientist.

I think this course is very useful and relevant for data scientists and analysts.

Recommended strongly for data scientists Excellent, but I would be grateful if you could translate all your courses of absolute quality into Spanish.

the learned material is applicable to any scientific work to be done Good intro to concepts The course very good for beginner data scientist Very nice course, learn a lot with it.

Everyone should know this, every thing should have prove and balance A great course which might not draw the right attention while moving towards a data scientist role.

latex is better :O Excelente All in all a great course with very valuable information to make a data scientist better at his job.

A must for each data scientist.

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final project

One of the best learning contents, great cour Final project was very intersting.

The final project was very interesting and taught me a lot about how I approach analysis projects, and how to improve this going forward.

The final project is challenging in terms of proper data cleaning, and it may take much more than 2 hours to complete it adequately.

The final project lacks clear directions.

My only qualm is that the final project was more difficult than I expected it to be given the content.

decent course, it is as long as you make it but start the final project early This course was very helpful for my career Good intro to the topic Reproducible research with doubt is important but videos and what it is discuss are not appealing and beyond that, what are worthen are the projects.

I struggled in getting the final project right but it helped me understand the course better.

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too much

A bit too much focused on academic research, I find.

Didn't enjoy cleaning data too much :) Reproducibility is one of the key elements of modern scientific method.

Important and interesting stuff - but lots of it is repeated too much, which make it seem like 4 weeks is too much for the material.

nice Awesome course Interesting course, but course assginments lack guidance, have too much complexity and require a time spent too long compared to the benefits Nice course.

ok, but the focus is too much on knitr, My experience of taking this course was really challenging and great, today I got to know how it is critical and crucial to Reproduce exact result as per author original research.

Nice course to explore Too much repetition; one video has been stretched into 10.

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data science specialization

Reproducible Research is the fifth course in the Data Science specialization, and the last course in what could reasonably be considered the basic R introduction portion of the series.

A few of the lectures were a bit repetitive if you are taking the full data science specialization.

Nice course Good course as part of the data science specialization.

A must for every analyst for its simple tips on reproducibility, which can go a very very long way at work or school In my opinion, this is one of the most valuable courses in the Data Science Specialization.

There is some nice general discussions about data science by the teacher, there is the explanation of the package knitr, and little else.As part of the data science specialization it is nice.

By far the most time consuming, yet rewarding course in the data science specialization thus far.

I only took this course because it is part of data science specialization.

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previous courses

Very interesting course, I was able to apply what I learned in the previous courses of the specialization, and that was a good exercise.

Not much to take in this course comparing to the previous courses.

Interesting material, but wasn't necessarily of the same depth of knowledge like previous courses in the series Best course, I have come cross.

Slightly less information than the previous courses in DS spec but important for someone who has not done scientific research in the past.

However, it is not quite as applicable as the previous courses for those who are individual contributors in the private sector and rarely have others double check their analyses or need to publish anything.

And I'm giving it 1 star so you can imagine how the previous courses are.

I like the complexity of this one and how it builds upon previous courses.

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Rating 4.3 based on 402 ratings
Length 5 weeks
Effort 4-9 hours/week
Starts Oct 5 (3 weeks ago)
Cost $50
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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