Harvard University - Principles, Statistical and Computational Tools for Reproducible Data Science
- Offered byedX
Principles, Statistical and Computational Tools for Reproducible Data Science at edX Overview
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  |
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
Principles, Statistical and Computational Tools for Reproducible Data Science at edX Course details
- This course is designed for students and professionals in biostatistics, computational biology, bioinformatics, and data science.
- 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.
- 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
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