subject
Intro

edX: Principles, Statistical and Computational Tools for Reproducible Science

 with  Curtis Huttenhower , John Quackenbush , Lorenzo Trippa and Christine Choirat

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.

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!

Syllabus

Module 1: Introduction to Course
  • Overview
  • Introduction to faculty
  • Project assignment
Module 2: Fundamentals of Reproducible Science
  • Why reproducible research matters
  • Definitions and concepts
  • Factors affecting reproducibility
Module 3: Case Studies in Reproducible Research
  • Potti 2006
  • Baggerly and Coombes 2007
  • Ioannidis 2009
  •  Reproducible Reporting
Module 4: Data Provenance
  • Project design
  • Journal requirements and mechanisms
  • Repositories
  • Privacy and security
Module 5: Statistical Methods for Reproducible Science
  • Prediction Models
  • Coefficient of determination
  • Brier score
  • AUC
  • Concordance in survival analysis
  • Cross validation
  • Bootstrap
Module 6: Computational Tools for Reproducible Science
  • R and Rstudio
  • Python
  • Git and GitHub
  • Creating a repository
  • Data sources
  • Dynamic report generation
  • Workflows
Course Conclusion
  • Final Project: Write a reproducible report that could be submitted at a peer review journal
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Cost Free Online Course
Subject Data Analysis
Institution Harvard University
Provider edX
Language English
Certificates $99 Certificate Available
Hours 3-8 hours a week
Calendar 8 weeks long

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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

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