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Harvard University - Principles, Statistical and Computational Tools for Reproducible Data Science 

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Principles, Statistical and Computational Tools for Reproducible Data Science
 at 
edX 
Overview

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

Duration

13 hours

Start from

Start Now

Total fee

Free

Mode of learning

Online

Schedule type

Self paced

Difficulty level

Intermediate

Official Website

Explore Free Course External Link Icon

Credential

Certificate

Principles, Statistical and Computational Tools for Reproducible Data Science
 at 
edX 
Highlights

  • 40% got a tangible career benefit from this course
  • Instructors - Curtis Huttenhower, John Quackenbush, Lorenzo Trippa, & Christine Choirat
  • Effort - 3?8 hours per week
  • FREE
    Add a Verified Certificate for ?7,386
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Principles, Statistical and Computational Tools for Reproducible Data Science
 at 
edX 
Course details

Who should do this course?
  • This course is designed for students and professionals in biostatistics, computational biology, bioinformatics, and data science.
What are the course deliverables?
  • It will cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.
More about this course
  • To meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery. This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project.

Principles, Statistical and Computational Tools for Reproducible Data Science
 at 
edX 
Curriculum

Module 1: Introduction to Course Overview Introduction to faculty Project assignment

Module 2: Fundamentals of Reproducible Science Why reproducible research matters Definitions and concepts Factors affecting reproducibility

Module 3: Case Studies in Reproducible Research Potti 2006 Baggerly and Coombes 2007 Ioannidis 2009 Reproducible Reporting

Module 4: Data Provenance Project design Journal requirements and mechanisms Repositories Privacy and security

Module 5: Statistical Methods for Reproducible Science Prediction Models Coefficient of determination Brier score AUC Concordance in survival analysis Cross validation Bootstrap

Module 6: Computational Tools for Reproducible Science R and Rstudio Python Git and GitHub Creating a repository Data sources Dynamic report generation Workflows Course Conclusion

Final Project: Write a reproducible report that could be submitted at a peer review journal

Principles, Statistical and Computational Tools for Reproducible Data Science
 at 
edX 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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    Principles, Statistical and Computational Tools for Reproducible Data Science
     at 
    edX 

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