this is intended to provide you with orientation and assistance during the project.
- Define cleare goals and responsibilities for individual milestones and subtasks.
- Define a fixed meeting at least every two weeks to discuss progress together
- if you have any questions, contact your mentor
The following tools should make it easier for you to work together
- Trello: Use it to define goals and tasks and responsibilities (https://trello.com/home)
- Git resp. Github: in consultation with your mentor and the Techlabs team, an official Techlabs project repo where you can work together on your code and version it with git - just like real software development. (https://github.com/)
We recommend to use the following
- R Studio for R: https://www.rstudio.com/
- Pycharm (Community) for Python: https://www.jetbrains.com/pycharm/
- Or more general Visual Studio Code: https://code.visualstudio.com/
- Python Basics: Pandas, SciPy, Numpy, Matplotlib
- Python Machine Learning: scikit-learn, Keras, Pytorch, Tensorflow
- Python: Flask, Django
- JavaScript: React, Vue, Angular
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed
│ ├── processed <- The final, canonical data sets for modeling
│ └── raw <- The original, immutable data dump
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks
│
├── references <- Data dictionaries, manuals, and all other explanatory materials
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment,
│ e.g. generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── data <- Scripts to download or generate data
│ └── make_dataset.py
│
├── features <- Scripts to turn raw data into features for modeling
│ └── build_features.py
│
├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ ├── predict_model.py
│ └── train_model.py
│
└── visualization <- Scripts to create exploratory and results oriented visualizations
└── visualize.py
Take some time to search for for smaller examples that are already publicly available on Github, so you can get a quick first overview of how different problems could be solved.
- DigitalOcean: https://www.digitalocean.com/
- Heroku: https://www.heroku.com/
Happy Coding :-)