/2020_06_CA_Astro_Data_Science_Workshop

Tutorials and notebooks dedicated towards teaching about Data Science and Machine Learning

Primary LanguageJupyter NotebookMIT LicenseMIT

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CA Astro - Data Science and Machine Learning Workshop

Tutorials and notebooks dedicated towards teaching about Data Science and Machine Learning
Author Victor Calderon [homepage]
Dates 13th and 14th of June, 2020
Documentation Link

Description

The following set of tutorials form part of the "Tutorials Series" hosted by "Central American - Caribbean Bridge in Astrophysics Program".

In these tutorials, we will cover how to:

  • Extract datasets
  • Perform exploratory data analysis (EDA) of the datasets. This includes
    1. Extract summary statistics about the dataset
    2. Clean the datasets
    3. Transform columns to useful features
  • Define and train a machine learning (ML) model using out-of-the-box utilities from Python packages.
  • Determine the performance of the ML model

We will also discuss some common practices when dealing with ML models, as EDA best practices.

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.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── 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
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

Project based on the cookiecutter data science project template. #cookiecutterdatascience