Principles, Statistical and Computational Tools for Reproducible Data Science - edX Course

Today the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.

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.

We’ll 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.

Consider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure - and success - stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research, and to encourage and integrate with similar environments for your collaborators and colleagues. We look forward to seeing you in this course and the research you do in the future!


Looking for more? Check out the National Institute of General Medical Sciences (NIGMS) - Clearinghouse for Training Modules to Enhance Data Reproducibility

In January 2014, NIH launched a series of initiatives to enhance rigor and reproducibility in research. As a part of this initiative, NIGMS, along with nine other NIH institutes and centers, issued a funding opportunity announcement (FOA) RFA-GM-15-006 to develop, pilot and disseminate training modules to enhance data reproducibility. This FOA was reissued in 2018 (RFA-GM-18-002).

For the benefit of the scientific community, NIGMS will be posting the products of grants funded by these FOAs HERE as they become available. In addition, NIGMS is sharing other relevant training modules developed, including courses developed from administrative supplements to NIGMS predoctoral T32 grants.

Learning Objectives
  • Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.
  • Fundamentals of reproducible science using case studies that illustrate various practices
  • Key elements for ensuring data provenance and reproducible experimental design
  • Statistical methods for reproducible data analysis
  • Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows.
  • How to develop new methods and tools for reproducible research and reporting
  • How to write your own reproducible paper.
Competencies
Scientific concepts and research design
Data management and informatics
Communication and teamwork
Organization
Fee
No
Availability
On demand
Delivery Method
Online

Submitted by Russell Lackey, MS on Tue, 11/19/2019 - 11:17 . Last modified on 11/19/2019