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Data Science Intermediate

Principles, Statistical and Computational Tools for Reproducible Data Science

Principles, Statistical and Computational Tools for Reproducible Data Science

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Curtis Huttenhower
Created by Curtis Huttenhower
8 weeks
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What you'll learn

Master these skills and apply them to real-world projects.

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.

Course curriculum

2 sections · 4 lessons

2 Sections
4 Lessons
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About this course

Course description

Meet your instructors

Learn from industry experts

Photo of Curtis Huttenhower

Curtis Huttenhower

Associate Professor of Computational Biology and Bioinformatics at Harvard University
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Photo of John Quackenbush

John Quackenbush

Professor of Computational Biology and Bioinformatics at Harvard University
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Photo of Lorenzo Trippa

Lorenzo Trippa

Associate Professor of Biostatistics at Harvard University
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Photo of Christine Choirat

Christine Choirat

Research Associate at Harvard University
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