2025-2026

1 General instructions

For this course, the grading consists of a long-term project.

You will need to work all along the semester to master it. Please start to work on it as soon as possible. You need to use your GitHub account (see Git lecture) as the expected outputs for this course are GitHub repositories.

2 Team project:

It will be mostly a visualization project, hopefully an interactive one, with the creation of an app or website, helping to navigate your work. The project consists of an investigation of an economic, social, ecological or public health subject (of your choice), through the analysis of (public) datasets. Concrete examples could be:

sources of energy, see this webpage or this app
population/demographic issues, see this advanced interractive app
an ecological issue like this one
sncf traffic, …

Bonus: one can also make predictions (future population in an area, future electric consumption, …) developping a Machine Learning/Statistical model and presents these predictions in the app/website.

2.1 Some source of datasets:

Here some dataset website are listed:

2.2 Setting and objectives

The group composition is available on Moodle.

No free-rider tolerated

The project repository must show a balanced contribution between group members and intra-group grade variation could be made to reflect issues on the intra-group workload balance. Group issues must be reported early to the teaching team.

2.3 Timing

Mid-term project snapshot: This part consists of starting the group project, explaining the main question of interest chosen to investigate. You should show preliminary work and organization of the workload, git first steps for the group project, etc. See details below Section 2.3.1. This part corresponds to 25% of the final grade. The due date is October 25 (23:59).
The GitHub repository with the presentations slides should be completed before Wednesday 10 December (23:59). This part corresponds to 40% of the final grade. Nothing pushed after the deadline will be taken into account.
The oral presentation (15mn + 5mn questions) December 08 (8:00) (SC 36.04). This part corresponds to 35% of the final grade.

2.3.1 Mid-term project snapshot

Please provide the URL of the group repository and the group composition (do not forget your student number) in Moodle.

The main point for this step is to create a README.qmd in a roadmap directory at the root of your project. Your file should give the outline of the project with the following ingredients:

You have to choose a name for your project. Hereafter, it is denoted by my_module_name.
description of a minimum viable project, showing the architecture (website, files, classes, etc.). It does not have to be functional at this stage but must have the main files needed, details of the coding pipeline, choice of the packages/technologies used, etc.
Create simple pictures (possibly camera pictures of handmade drawings) showing the results you want to create (time series, maps, etc.).
Provide several git branches (at least 2) so the project can move forward independently.
Retro planning with a Gantt diagram as follows (see for instance Quarto and Mermaid):

    gantt
        title Preparing Polyglot Notebooks Talk for Stir Trek 2023
        axisFormat %m-%d
        section Proposal <br> and <br> Evaluation
            Submit Abstract         :done,                2023-01-15, 2023-02-18
            Session Evaluation      :done, EVAL,          2023-02-18, 2023-03-05
            Talk Accepted           :milestone, done,     after EVAL,
        section Talk <br> Preparation
            Research & Outlining    :done, OUTLINE,       2023-03-12, 9d
            Create Mermaid Examples :done, MER_EXAMPLE,   after OUTLINE, 5d
            Write Mermaid Articles  :active, MER_ART,     after MER_EXAMPLE, 7d
            Write Jupyter Articles  :                     after MER_ART, 3d
        section Delivery
            Final Notebook          :crit, NOTEBOOK,      2023-04-19, 7d
            Rehearsal               :crit,                after NOTEBOOK, 2023-05-04
            Stir Trek 2023          :milestone, crit,     2023-05-05, 1d

2.3.1.1 Elements expected for the mid-term project

General	Details	Points (out of 20)
Mid-term	Git / branches	4
	Task affectation / Gantt chart	4
	Dataset choices / Download / Description	4
	Packages/software description for the project	4
	Figure of interest/narration	4
Total		20

2.3.2 Final project

2.3.2.1 General guidelines

The ultimate goal is to provide one of the two outputs:

a runnable Python app that illustrates your data and the narration.
a website with quarto (or other libraries) that presents your project. This website should be deployed through GitHubPages. At least one of the pages should contain an interactive element (maps, widgets, etc.).

A description of the procedure will be needed (imagine you are addressing a user not aware of your package). An example of a project made in 2020, is available at https://github.com/tanglef/chaoseverywhere.

2.3.2.2 Project structure

All the code will be placed in a subdirectory called /my_module_name (choosing your module name accordingly).
A slide deck in Beamer/LaTex, quarto or LibreOffice will be put in a /slide directory of the repository. The latter will be a short presentation of the work that will be orally presented during 15mn in front of a jury, at the end of the project.

2.3.2.3 Git aspects

A .gitignore that prevents garbage files from being included in your project.
Equilibrated commits in two branches should be done (e.g., in the development branch and the master one), and merged for the milestone day.
Your repository should contain a README.md:
- containing the title and a short description of the project
- Source of data
- for those who choose the module/app, a description of how to run/install it
- for those who choose a website, the link to the website and a code snippet to build it
- authors list and a license description. See this website. A default choice could be MIT Licence.

2.3.2.4 Object programming aspects

You should code at least one Python class.
Your python project should contain submodules. Each submodule will be devoted to a specific sub-task of your project.

2.3.2.5 Dataset(s)

For reproducibility, the data should be easily available to anyone that want to check your work. It is better if the data used should be available in a way that the end user does not need to perform a manual download of any kind (use the pooch package or variants for instance).

2.3.2.6 Time/memory evaluation

A full study of the time and memory footprint of the code produced will be provided for the whole pipeline used. Elements showing speed-up / memory savings you could find along the way would be also appreciated.

2.3.2.7 Documentation

Docstrings should be populated for every python class and function.
You should create an API documentation using sphinx for instance.

2.3.2.8 Test and CI

Provide unitary tests to check that the function you proposed satisfies the requirement you target.
Bonus point will be given if you implement a Continuous Integration solution with GitHub that runs your unitary test at each commit.

Here’s a recap for the final project (code, app/website):

General	Details	Points (/20)
Code	Problem Resolution/Narration	3
	Readme/Installation	2
	Unit Tests	3
	Class (create at least 1 class)	2
	Code quality/Organization	2
	Graphical aspects: Widgets, clickable map, etc.	4
	Git branches	2
	Documentation	2
Total·		20

Bonus will be given for the following reasons:

Continuous integration with Github
A code very object-oriented (inheritance, abstraction, …)
Automatic loading of dataset (with pooch for instance)
Time/Memory efficiency assessment
Originality

Here’s also a short recap for the presentation part (slides, oral presentation):

General	Details	Points (/20)
Oral	Slides quality and structure	10
	Clarity / lively presentation / Rhythm / Show	10
Total·		20

3 Examples

Here are some links to the Github pages showing what have been done the last year:

Group 1 Group 2 Group 3