Data science is Software: #lifehacks for the Jupyer data scientist

Materials for the 1.5 hr talk: (Updated for ODSC East 2017)

• Slides
• Commandline Demo Script
• Jupyter Notebook
• Example project that was created during the talk

.
├── data
│   └── raw
│       ├── pumps_train_labels.csv
│       └── pumps_train_values.csv
├── LICENSE
├── notebooks
│   ├── labs
│   │   ├── 2.0-environment-lab.ipynb
│   │   ├── 2.0-environment-solution.ipynb
│   │   ├── 3.0-refactoring-lab.ipynb
│   │   ├── 3.0-refactoring-solution.ipynb
│   │   └── 4.0-testing-lab.ipynb
│   └── lectures
│       ├── 2.0-environment.ipynb
│       ├── 3.0-refactoring.ipynb
│       └── 4.0-testing.ipynb
├── README.md
├── requirements.txt
├── setup.cfg
└── src
    ├── features
    │   ├── build_features.py
    │   └── preprocess_solution.py
    ├── __init__.py
    ├── mcmc
    │   └── hamiltonian.py
    ├── model
    │   └── train_model_solution.py
    ├── tests
    │   ├── example.py
    │   ├── __init__.py
    │   ├── test_example.py
    │   └── test_lab4_solutions.py
    └── utils.py