Course description
Principles, Statistical and Computational Tools for Reproducible Data Science
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.
Upcoming start dates
Who should attend?
Prerequisites
- Basic knowledge of Rand Git
- A computer that is capable of downloading software to run on it.
Training content
Introduction to Reproducible Science
Fundamentals of Reproducible Science
- Definitions and Concepts
- Factors affecting reproducibility
Case Studies in Reproducible Research
Data Provenance
- Project Design
- Journal Requirements
- Repositories
- Privacy and Security
Computational Tools for Reproducible Science
- R and Rstudio
- Python, Git, and GitHub
- Creating a repository
- Data sources
- Dynamic report generation
- Workflows
A optional deeper dive into Statistical Methods for Reproducible Science
- Prediction Models
- Coefficient of determination
- Brier score
- Area Under the Curve (AUC)
- Concordance in survival analysis
- Cross-validation
- Bootstrap
- Simulations
- Clustering
Course delivery details
This course is offered through Harvard University, a partner institute of EdX.
3-8 hours per week
Costs
- Verified Track -$99
- Audit Track - Free
Certification / Credits
What you'll learn
- 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.
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