Project-Guide

this is intended to provide you with orientation and assistance during the project.

Project-Management

• 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

Collaboration Tools

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/)

Integrated Development Environment (the place where to do the coding)

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/

Useful Frameworks

• Python Basics: Pandas, SciPy, Numpy, Matplotlib
• Python Machine Learning: scikit-learn, Keras, Pytorch, Tensorflow

Backend Frameworks

• Python: Flask, Django

Frontend Frameworks

• JavaScript: React, Vue, Angular

Data Science Code Structure Example

Project Organization

├── 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

---

More Information

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.

Deployment

• DigitalOcean: https://www.digitalocean.com/
• Heroku: https://www.heroku.com/

Happy Coding :-)