Moved to https://github.com/thedatasociety/lab-dataviz

Associated examples and material of the discipline Data Visualization -- Data Science Specialization Program -- FACENS.

• Resources
• Running Examples & Exercises
  • Cloud Services
    • MyBinder (project BinderHub)
    • Google Colaboratory
  • Locally
    • Local installation
    • Using Docker

• Slack
• Canvas
• Kaggle

Examples and exercises are presented as Jupyter Notebooks. Get started by cloning this repository within your own Jupyter environment or by launching a Jupyter environment from cloud services.

• Jupyter Lab:
  

• Jupyter:
  

• Jupyter Notebook:
  

• Please follow these instructions for installing and running Jupyter using Anaconda.

• Please follow these instructions for installing and running Jupyter using pip.

Alternatively, you may install Jupyter Lab by following these instructions.

You can run a containerized instance of Jupyter Lab from our own Docker image. Try:

docker run -it -p 8888:8888 matheusmota/facens-dataviz

Alternatively, you can map you local home folder into the container:

docker run -it -v `pwd`:/jupyter/data/ -p 8888:8888 matheusmota/facens-dataviz

Access the Jupyter Lab server by going to https://0.0.0.0:8888.

The Dockerfile used to build the image can be found here.

Alternative #3: repo2docker

repo2docker -p 8888:8888 -v `pwd`:`pwd`  https://github.com/matheusmota/facens-dataviz  jupyter-lab --ip 0.0.0.0 --NotebookApp.token=''

Access the Jupyter Lab server by going to https://0.0.0.0:8888/lab.

• Feel free to use images from this library of ready-to-run Docker images containing Jupyter. Do not forget to install dependencies (see the binder folder).