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Harvard University via edX

Principles, Statistical and Computational Tools for Reproducible Data Science

4.314 ratings at edX

Overview

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.

What you'll learn

  • Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproduc…
  • 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…
  • How to develop new methods and tools for reproducible research and reporting
  • How to write your own reproducible paper.

Syllabus 21

  1. Definitions and Concepts
  2. Factors affecting reproducibility
  3. Project Design
  4. Journal Requirements
  5. Repositories
  6. Privacy and Security
  7. R and Rstudio
  8. Python, Git, and GitHub
  9. Creating a repository
  10. Data sources
  11. Dynamic report generation
  12. Workflows
  13. Prediction Models
  14. Coefficient of determination
  15. Brier score
  16. Area Under the Curve (AUC)
  17. Concordance in survival analysis
  18. Cross-validation
  19. Bootstrap
  20. Simulations
  21. Clustering

Skills you'll practise

BioinformaticsR (Programming Language)Applied MathematicsSoftware Design PatternsAstrophysicsResearch MethodologiesPolitical SciencesPublic HealthRStudioStatistical MethodsStatisticsResearchBiostatisticsGithubGit (Version Control System)Data ScienceComputational ToolsComputational BiologyLife SciencesCase StudyPresentations

Advantages and disadvantages

Advantages

  • One of the best-taught programming courses in the world, by David J. Malan and his team.
  • Problem sets are real projects that stay on your GitHub.
  • Free certificate from CS50 when you finish on cs50.harvard.edu (the edX certificate is paid).
  • Active community on Discord, Reddit and Ed for help at any hour.
  • University courses you can audit for free, with lectures, readings and practice quizzes.
  • A verified certificate from the university if you pay for it.
  • Taught by Harvard University, one of the strongest names in its field.
  • 117,201 learners have taken it, so help and notes are easy to find.

Disadvantages

  • Problem sets are hard and take far longer than the videos; plan 10–20 hours a week.
  • Lectures are long (about 2 hours each).
  • Graded assignments and the certificate need the paid track.
  • Audit access can expire a few weeks after the course ends.
  • Learning is free, but the certificate costs money.
  • Some parts (graded work, certificate) are paid.

Some points apply to every course of this kind; see how we rank.

Free to audit

  • Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
  • Paid: Graded assignments and the verified certificate (Certificate $149). Audit access may end after the course closes.

Before you start

Basic knowledge of Rand Git A computer that is capable of downloading software to run on it.

Taught by

  • Curtis HuttenhowerAssociate Professor of Computational Biology and Bioinformatics
  • John QuackenbushProfessor of Computational Biology and Bioinformatics
  • Lorenzo TrippaAssociate Professor of Biostatistics
  • Christine ChoiratResearch Associate

Harvard University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (98/100). CS50 and HarvardX are among the best-taught courses anywhere.

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