This course is the last course in a series of modules for data science. This course assumes you have done the introduction in Python and the Data Analyses & Visualisation course https://github.com/raoulg/MADS-DAV
For this project you will need some dependencies.
The project uses python 3.10, and all dependencies can be installed with pdm if you are using that with pdm install.
The lessons can be found inside the notebooksfolder.
The source code for the lessons can be found in the srcfolder.
├── README.md <- This file
├── .gitignore <- Stuff not to add to git
├── .lefthook.yml <- Config file for lefthook
├── pyproject.toml <- Human readable file. This specifies the libraries I installed to
| let the code run, and their versions.
├── pmd.lock <- Computer readable file that manages the dependencies of the libraries
├── data
│ ├── external <- Data from third party sources.
│ ├── processed <- The processed datasets
│ └── raw <- The original, raw data
│
├── models <- Trained models
│
├── notebooks <- Jupyter notebooks. Naming convention is xx_name_of_module.ipynb where
│ xx is the number of the lesson
│
├── references <- Some background information on codestyle, and a courseguide
│
├── reports <- Generated analysis like PDF, LaTeX, etc.
└── figures <- Generated graphics and figures to be used in reporting
The .lefthook.yml file is used by lefthook, and lints & cleans the code before I commit it. Because as a student you probably dont commit things, you can ignore it.
I have separated the management of datasets and the trainingloop code. You will find them as dependencies in the project:
Both of these will be used a lot in the notebooks; by separating them it is easier for students to use the code in your own repositories.