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Principles, Statistical and Computational Tools for Reproducible Data Science

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.

There is one session available:

84,410 already enrolled! After a course session ends, it will be archived.
Starts Dec 15
Estimated 8 weeks
3–8 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

About this course

Skip About this course

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!

At a glance

  • Institution: HarvardX
  • Subject: Data Analysis & Statistics
  • Level: Intermediate
  • Prerequisites:
    • Basic knowledge of Rand Git
    • A computer that is capable of downloading software to run on it.
  • Language: English
  • Video Transcript: English

What you'll learn

Skip 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
  • How to write your own reproducible paper.

Module 1: Introduction to Reproducible Science

Module 2: Fundamentals of Reproducible Science

  • Definitions and Concepts
  • Factors affecting reproducibility

Module 3: Case Studies in Reproducible Research

Module 4: Data Provenance

  • Project Design
  • Journal Requirements
  • Repositories
  • Privacy and Security

Module 5: Computational Tools for Reproducible Science

  • R and Rstudio
  • Python, Git, and GitHub
  • Creating a repository
  • Data sources
  • Dynamic report generation
  • Workflows

Module 6: A optional deeper dive into Statistical Methods for Reproducible Science

  • Prediction Models
  • Coefficient of determination
  • Brier score
  • Area Under the Curve (AUC)
  • Concordance in survival analysis
  • Cross-validation
  • Bootstrap

  • Simulations

  • Clustering

About the instructors

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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