- Indicate your name on the PAD.
- Register on the Mattermost : @sarahbrb
- Set up a public github repository for this lecture.
- Register to the MOOC.
- Follow modules 1 + 2 of the MOOC with as much exercises as possible.
- Set up a computational document system (e.g., Rstudio or Jupyter) on your laptop.
- Report the URL of your git project, your mattermost ID on the PAD.
- Start learning R by reading my short R crash course for computer scientists.
- Read Popper’s text and write a short summary in your GitHub repository.
- Criticize every figure of Jean-Marc’s slides by:
- i. Applying the checklist for good graphics : Checklist 01, Checklist 02;
- ii. Proposing a better representation (hand-drawing is fine) that passes the checklist.
- Report this work for at least 3 figures on you github/gitlab project.
- MOOC: Complete exercise 5 of module 2 (Challenger). Write a short text explaining what is good and wrong about this document (you may want to provide an updated version of the notebook and upload on your github/gitlab space.
- Continue the hands-on, improve the experiment design and the analysis.
- Compute confidence intervals for the data of Parallel Quicksort
- Fit a linear model for the data in Parallel Quicksort
- (For the 28th of November) Read the articles provided here (about 5 min for each paper) and :
- i. Explain how and why citations are used.
- ii. Qualify the journal in a few words.
- Keep building intuition on linear model
- Try to complete the peer-evaluation of the MOOC