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There are 4 modules in this course
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.
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.
What's included
9 videos4 readings1 assignment
Show info about module content
9 videos•Total 72 minutes
Introduction•2 minutes
What is Reproducible Research About?•8 minutes
Reproducible Research: Concepts and Ideas (part 1)•7 minutes
Reproducible Research: Concepts and Ideas (part 2) •5 minutes
Reproducible Research: Concepts and Ideas (part 3) •3 minutes
Scripting Your Analysis •5 minutes
Structure of a Data Analysis (part 1)•12 minutes
Structure of a Data Analysis (part 2)•18 minutes
Organizing Your Analysis•11 minutes
4 readings•Total 32 minutes
A Note of Explanation•2 minutes
Syllabus•10 minutes
Pre-course survey•10 minutes
Course Book: Report Writing for Data Science in R•10 minutes
1 assignment•Total 30 minutes
Week 1 Quiz•30 minutes
Week 2: Markdown & knitr
Module 2•2 hours to complete
Module details
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.
What's included
9 videos1 assignment1 peer review
Show info about module content
9 videos•Total 59 minutes
Coding Standards in R•9 minutes
Markdown•5 minutes
R Markdown•7 minutes
R Markdown Demonstration•7 minutes
knitr (part 1)•7 minutes
knitr (part 2) •4 minutes
knitr (part 3) •5 minutes
knitr (part 4) •9 minutes
Introduction to Course Project 1•5 minutes
1 assignment•Total 30 minutes
Week 2 Quiz•30 minutes
1 peer review•Total 60 minutes
Course Project 1•60 minutes
Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
Module 3•1 hour to complete
Module details
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.
What's included
10 videos
Show info about module content
10 videos•Total 60 minutes
Communicating Results•7 minutes
RPubs •3 minutes
Reproducible Research Checklist (part 1)•8 minutes
Reproducible Research Checklist (part 2) •10 minutes
Reproducible Research Checklist (part 3) •7 minutes
Evidence-based Data Analysis (part 1)•4 minutes
Evidence-based Data Analysis (part 2) •4 minutes
Evidence-based Data Analysis (part 3) •4 minutes
Evidence-based Data Analysis (part 4) •5 minutes
Evidence-based Data Analysis (part 5) •8 minutes
Week 4: Case Studies & Commentaries
Module 4•2 hours to complete
Module details
This week there are two
case studies involving the importance of reproducibility in science for you to watch.
What's included
5 videos1 reading1 peer review
Show info about module content
5 videos•Total 59 minutes
Caching Computations•11 minutes
Case Study: Air Pollution•14 minutes
Case Study: High Throughput Biology•31 minutes
Commentaries on Data Analysis•2 minutes
Introduction to Peer Assessment 2•1 minute
1 reading•Total 10 minutes
Post-Course Survey•10 minutes
1 peer review•Total 60 minutes
Course Project 2•60 minutes
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R
RG
5·
Reviewed on Apr 29, 2020
Great topic which is discussed well with a good case study. I'd like to see more up-to-date content and more detailed analytical techniques. However, it's a nice introduction!
I
IM
5·
Reviewed on Aug 9, 2019
Without taking this course wouldn't have fully understood the importance of reproducible research in data science. Thank you so much. I recommend this course for all data scientists.
M
MF
5·
Reviewed on Mar 30, 2022
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.
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