Principles, Statistical and Computational Tools for Reproducible Data Science
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Brief Introduction
Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.Course Summary
Learn the principles of statistical and computational tools for reproducible data science in this Harvard course. Gain hands-on experience in data manipulation, visualization, and analysis using R programming language and version control with Git and GitHub.Key Learning Points
- Understand the importance of reproducibility in data science
- Learn R programming language for data manipulation, visualization, and analysis
- Gain experience in version control with Git and GitHub
Related Topics for further study
Learning Outcomes
- Ability to apply statistical and computational tools for reproducible data science
- Proficiency in R programming language for data manipulation, visualization, and analysis
- Experience in version control with Git and GitHub
Prerequisites or good to have knowledge before taking this course
- Familiarity with basic statistical concepts
- Basic knowledge of programming
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Science Essentials
- Data Science: Machine Learning
- Data Science: Probability
Related Education Paths
Notable People in This Field
- Hadley Wickham
- Karl Broman
Related Books
Description
Course description
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 intensive data 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!
Knowledge
- 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, and how to write your own reproducible paper.
Summary of User Reviews
Discover the Principles of Statistical and Computational Tools for Reproducible Data Science in this Harvard online course. Users have rated it highly for its practicality and relevance. Learn from the best and gain valuable skills to apply to your work and research.Key Aspect Users Liked About This Course
Many users appreciated the practicality and relevance of the course content.Pros from User Reviews
- Course content is well-organized and easy to follow.
- Instructors are knowledgeable and engaging.
- Great opportunity to learn from and collaborate with peers.
- Practical skills learned can be applied to real-world projects.
- Course materials are comprehensive and thorough.
Cons from User Reviews
- Some users found the course to be too basic or introductory.
- The course may not be suitable for those without a strong background in statistics or programming.
- Some users found the assignments to be too time-consuming.
- The pace of the course may be too slow for some learners.
- Some users felt that the course lacked depth in certain areas.
Principles, Statistical and Computational Tools for Reproducible Data Science
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Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others....
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In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). In this course you will learn to recognize, design, and use effective visualizations....
