Update README

Author: ghurault

README.md

@@ -9,7 +9,7 @@ This repository serves as a personal template for data science projects.
 - Analysis scripts and notebooks are located in [`analysis/`](analysis/).
 - Reusable functions and modules are stored in the local package [`src/`](src/).
   - The package can then be installed in development mode with `pip install -e .` for easy prototyping.
-  - [`src/config.py`](src/config.py) can be used to store variables, constants and configurations.
+  - [`src/config.py`](src/config.py) is used to store variables, constants and configurations.
 - Tests for functions in [`src/`](src/) should go to [`tests/`](tests/) and follow the convention `test_*.py`.
 
 Moreover, I use the following the directories that are (usually) ignored by Git:
@@ -58,15 +58,31 @@ In particular, I use:
 
 ### Possible extensions
 
+The `src/` package could contain the following modules or sub-packages depending on the project:
+
+- `utils` for utility functions.
+- `data_processing` for data processing functions (this could be imported as `dp`).
+- `features`: for extracting features.
+- `models`: for defining models.
+- `evaluation`: for evaluating performance.
+- `plots`: for plotting functions.
+
 The repository structure could be extended with:
 
 - `docs/` to store documentation (e.g. of the `src` package). For example, a documentation could be generated using [mkdocs](https://www.mkdocs.org/) or [quartodoc](https://machow.github.io/quartodoc/get-started/overview.html).
-- `data-raw/` to store data processing functions whose output is stored in [`data/`](data/). For example when data is scrapped from the web and cleaned before saving it.
+- subfolders in `data/` such as `data/raw/` for storing raw data.
 - `models/` to store model files.
 
 ### Related
 
-This template is inspired by the concept of a [research compendium](https://doi.org/10.1080/00031305.2017.1375986).
+This template is inspired by the concept of a [research compendium](https://doi.org/10.1080/00031305.2017.1375986) and similar projects I created for R projects (e.g. [reproducible-workflow](https://github.com/ghurault/reproducible-workflow)).
 
 This template is relatively simple and tailored to my needs.
-More sophisticated templates are available elsewhere, such as the [Cookiecutter Data Science](https://github.com/drivendataorg/cookiecutter-data-science/) template.
+More sophisticated templates are available elsewhere, such as:
+
+- [Cookiecutter Data Science](https://github.com/drivendataorg/cookiecutter-data-science/).
+- [https://joserzapata.github.io/data-science-project-template/](https://joserzapata.github.io/data-science-project-template/)
+- [Data Science for Social Good's hitchikers guide template](https://github.com/dssg/hitchhikers-guide/tree/master/sources/curriculum/0_before_you_start/pipelines-and-project-workflow)
+- [https://github.com/khuyentran1401/data-science-template](https://github.com/khuyentran1401/data-science-template)
+
+As opposed to other templates, this template is more focused on experimentation rather than sharing a single final product.