General information & System setup


Prerequisites | Mandatory

  • You should bring your own laptop. This course teaches essential data analysis skills, including how to set up and manage your computing environment. Using your own laptop allows you to practice continuously and apply what you learn directly to your research.

  • An RStudio Server account will be used during the first three classes to introduce R.

  • From class 4 onward, you will install and configure R, RStudio, and Git on your own computer.

  • You must have administrator privileges on your computer in order to install software.

How to setup your system for reproducible data analysis

Reproducibility is a cornerstone of reliable data analysis, ensuring that results can be consistently replicated and built upon.

Setting up a system for reproducible data analysis requires careful planning and adherence to best practices. Bellow is an overview of the major steps a researcher should take into consideration.

Tools used for this course

In this course, we will use R and RStudio for data analysis, and Git and GitHub for version control:

  • R is a powerful programming language designed for statistical computing and data analysis.
  • RStudio is an integrated development environment (IDE) that provides a user-friendly interface for writing, debugging, and managing R code.
  • Git is a version control system that tracks changes in code, allowing for efficient collaboration and history management.
  • GitHub is a cloud-based platform for hosting Git repositories, enabling code sharing, collaboration, and version tracking across teams.

By combining these tools, researchers can streamline their workflows, ensure reproducibility, and foster collaboration and efficiency in data-driven projects.

Install software

  1. First install R: https://cran.r-project.org/
  2. Then install RStudio: https://posit.co/download/rstudio-desktop/
  3. Install git: https://git-scm.com/downloads
    - Detailed instructions for each Operating System (Linux, Mac, Windows) here.
  4. Create an account in GitHub: https://docs.github.com/en/get-started/start-your-journey/creating-an-account-on-github

Bibliography

Course final evaluation

Masters students

As outlined in the FUC, the final evaluation will take into account the following components:

  • Participation in class (10%);
  • Project progress discussed in each class (10%);
  • Final project report (40%), formatted in the style of a Nature Scientific Data article;
  • Final oral presentation (40%), consisting of a 10-minute presentation.

The final report should be submitted on the same day as the oral presentation. During the presentation, students will briefly introduce their dataset and present their quality assessment of the data.

The tutor will evaluate their overall performance in class, as well as the quality of their written report and oral presentation.

PhD students

As outlined in the FUC, the final evaluation will take into account the following components:

  • Participation in class (15%);
  • Final project report (35%), formatted in the style of a Nature Scientific Data article;
  • Final project oral presentation (35%), consisting of a 10-minute presentation;
  • Peer review of a fellow student’s final project report (15%) evaluated on the constructiveness, depth of feedback, critical analysis, clarity, originality, independent thought;
  • The report to be reviewed will be assigned at random, and the peer review will be carried out during the presentation by completing a peer-feedback form.

The final report should be submitted on the same day as the oral presentation. During the presentation, students will briefly introduce their dataset and present their quality assessment of the data.

The tutor will evaluate their overall performance in class, the quality of their written report and oral presentation, and their peer-feedback form.

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