Principles, Statistical and Computational Tools for Reproducible Data Science

The principles and techniques of reproducible research are more important than ever today, spanning disciplines as diverse as astrophysics and political science. Nobody wants to conduct research that cannot be replicated. As a result, this course is ideal for anyone conducting extensive data research. While many of us have a biomedical background, this course is intended for a wide range of data scientists.
This course will examine the fundamentals of methods and tools for reproducible research in order to meet the needs of the scientific community. You will participate in six modules led by experienced faculty from the Harvard T.H. Chan School of Public Health, which will include several case studies demonstrating the significant impact of reproducible research methods on scientific discovery.

KEY FEATURE

Recognize a set of concepts, thought patterns, analysis paradigms, and computational and statistical tools that, when combined, support data science and reproducible research.

Case studies illustrating various practises are used to teach the fundamentals of reproducible science. Important factors in ensuring data provenance and reproducible experimental design.

Computational tools and workflows for reproducible data analysis and version control, reproducible data, and reproducible dynamic report generation.

Learn how to create new methods and tools for reproducible research and reporting, as well as how to write your own reproducible paper.

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Key Topics

    Principles, Statistical and Computational Tools for Reproducible Data Science

    Students and professionals in biostatistics, computational biology, bioinformatics, and data science will benefit from this course. The course content will include video lectures, case studies, peer-to-peer interactions, and the use of computational tools and platforms (such as R/RStudio and Git/Github), with a final presentation of a reproducible research project.
    Fundamentals of Reproducible Science; Case Studies; Data Provenance; Reproducible Science Statistical Methods; Reproducible Science Computational Tools; and Reproducible Reporting Science will be covered. These concepts are intended to be applicable across the data sciences, including physical and life sciences, applied mathematics and statistics, and computing.
    Consider this topic a survey of best practises: we’d like to make you aware of potential pitfalls in reproducible data science, as well as some failure – and success – stories from the past, as well as tools and design patterns that could help make it all easier. However, it will ultimately be up to you to use the skills you learn in this course to create your own environment in which you can easily conduct reproducible research, as well as to encourage and integrate similar environments for your collaborators and colleagues. We look forward to seeing you in this course and in your future research!

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