SLLD Slides, code and other material - EMbeDS-education/ComputingDataAnalysisModeling20242025 GitHub Wiki
Module 1
Note: Materials for each Practicum may be updated and re-uploaded prior to the corresponding lecture (make sure you are accessing the most up to date materials).
Lecture 1 and 2:
Lecture 3:
Lecture 4:
Lecture 5:
- Smooothing Outline
- Smooothing Practicum
- Smooothing Info and Demos from a course by Rafael A. Irizarry
Lecture 6:
Lecture 6 and 7:
Module 2
Lecture 8:
Lecture 9:
Lecture 10:
Lecture 11:
Lecture 12:
Lecture 13:
Lecture 14:
Projects
Oral Presentations Session
Fri May 09, 2025, 3:00pm onward, L'EMbeDS Lab (Aula 3, Via Maffi)
- Group: Name, Name... (type of students). Modules. Project title.
- ...
- ...
Instructions for Oral Presentations
Think of this as a short conference talk. It should be polished, engaging and clear. In terms of content, make sure you include
- Motivation and background: what were the aims of your project, and what is the context – in terms of subject matter questions, and in terms of applicable statistical and computational tools.
- What has been accomplished: state clearly what you were able to accomplish during the course, illustrate methods used and results.
- Where are things going: elaborate on future plans; can you envision what you did during the course as the basis for a continuing project/collaboration?
Some parameters:
- Plan on ~20 minutes.
- Plan on ~10-15 slides.
- Have an additional final slide with a complete list of references.
- If you are working in a group, members should alternate speaking.
- Rehearse your presentation to make sure that it fits in the allotted time (and, if you are working in a group, that the transitions between members are smooth).
Written Reports
Due date: Mon May 19, 2025
- Group: Name, Name... (type of students). Modules. Report title.
- ...
- ...
Instructions for Written Reports
Loosely follow the format of a short journal article. Prepare the report including:
- Title
- Abstract (a summary with a clever spin)
- Introduction (articulate background and motivation)
- Methods
- Short description of data and preprocessing steps
- Short description of computational and statistical methodology used
- Results
- Description of results obtained; tables and figures
- Discussion
- Scientific interpretation of results, as applicable
- Description of future plans to pursue this line of research
- References (as complete as you can)
- Appendix/Supplement
- More details on Methods
- More details on Results; tables and figures not reported in the main text
- You may also report here techniques that you explored and discarded.
Try not to exceed 15 pages for your main text (this count does not include References and Appendix and Supplement).