Reproducible Data Science: Tools and Principles
Master reproducible research techniques and tools in this comprehensive Harvard data science course.
Master reproducible research techniques and tools in this comprehensive Harvard data science course.
Dive into the world of reproducible data science with Harvard's comprehensive course on principles and tools for trustworthy research. This program equips you with essential skills to ensure your research results are reproducible, reliable, and effectively communicated. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you'll explore fundamental methods and cutting-edge tools that support reproducible science across diverse disciplines. The course covers key topics including data provenance, statistical methods, computational tools, and reproducible reporting. Through a blend of video lectures, case studies, and hands-on projects using R/RStudio and Git/Github, you'll gain practical experience in applying reproducible research techniques. Whether you're a student, professional, or researcher in biostatistics, computational biology, or any data-intensive field, this course will enhance your ability to conduct trustworthy and impactful scientific research.
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اَلْعَرَبِيَّةُ, Deutsch, English, 9 more
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What you'll learn
Understand key concepts and principles of reproducible science
Apply statistical methods for ensuring reproducible data analysis
Master computational tools for version control and reproducible workflows
Develop skills in data provenance and reproducible experimental design
Create dynamic, reproducible reports using tools like RMarkdown and Jupyter
Analyze case studies illustrating the impact of reproducible research practices
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course provides a comprehensive introduction to reproducible data science principles and tools. Students will learn fundamental concepts of reproducible science, explore case studies illustrating best practices, and gain hands-on experience with key computational tools. The curriculum covers data provenance, statistical methods for reproducible analysis, version control systems, and techniques for generating reproducible reports. Participants will use tools such as R/RStudio, Git/GitHub, and dynamic report generation platforms. The course emphasizes practical application, culminating in a final project where students create their own reproducible research paper. By the end of the course, learners will have developed a robust skill set for conducting trustworthy, transparent, and reproducible scientific research across various data-intensive fields.
Introduction to Reproducible Science
Module 1
Fundamentals of Reproducible Science
Module 2
Case Studies in Reproducible Research
Module 3
Data Provenance
Module 4
Computational Tools for Reproducible Science
Module 5
Statistical Methods for Reproducible Science
Module 6
Fee Structure
Instructors
1 Course
Leading Computational Biologist Advancing Human Microbiome Research
Curtis Huttenhower, Associate Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health, has revolutionized our understanding of microbial communities and their impact on human health. After earning his Ph.D. in Genomics and Computational Biology from Princeton University, he has built an extraordinary career combining computational innovation with biological discovery. As director of the Huttenhower Lab and co-director of the Harvard Chan Microbiome in Public Health Center, he leads groundbreaking research on the human microbiome's role in health and disease. His work includes leadership roles in the NIH Human Microbiome Project and development of widely-used computational tools for microbiome analysis. His research has produced over 200 peer-reviewed publications, earning him recognition as a Highly Cited Researcher by Clarivate Analytics and the Presidential Early Career Award for Scientists and Engineers (PECASE). Through his innovative approaches combining machine learning, statistical methods, and biological experimentation, he continues to advance understanding of how microbial communities influence human health while mentoring the next generation of computational biologists.
1 Course
Leading Computational Biologist Revolutionizing Cancer Genomics and Data Science
John Quackenbush, Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health and Chair of the Department of Biostatistics, has transformed our understanding of genomics and cancer biology through innovative data analysis approaches. After earning his PhD in Theoretical Physics, he made a pivotal career shift in 1992 when he received a fellowship to work on the Human Genome Project, leading him through positions at the Salk Institute, Stanford, and The Institute for Genomic Research before joining Harvard in 2005. As director of the Center for Cancer Computational Biology at Dana-Farber Cancer Institute, he pioneers the use of massive datasets to understand how multiple small effects combine to influence human health and disease. His impressive research portfolio includes over 300 scientific papers with more than 73,000 citations, while his work spans from developing new analytical methods for microarray analysis to creating sophisticated network models of cancer biology. His achievements have earned him recognition as a White House Open Science Champion of Change in 2013, and his leadership extends to serving on multiple scientific advisory boards and directing major research initiatives in cancer genomics and precision medicine.
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4.1 course rating
11 ratings
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