We will first activate a virtual environment, and install all required dependencies prior to the start. The facilitator will ensure that all participants have completed this step before moving to the next topic.
In this section we will introduce the dataset and the problem statement. We will use the Pandas Python package to assess the presence of missing information, and get familiar with the content of the data via box plots, scatter plots and histograms using Plotly.
Once we are familiar with the data and generated a few sample visualizations, we will refactor our code into reusable functions that can be incorporated into a script. We will cover the anatomy of a Python script and interact with the script via the command line.
In this section, we will learn about the main components in a Dash app. We will introduce various dashboard designs (layouts).
In this section we will implement code needed to generate and deploy a dashboard exploring the selected data locally. We will explore the pitfalls (potential sources of bugs, interpreting and fixing errors as they appear in the dashboard) and implement various layouts.
We will learn about files needed to deploy a dashboard online: Procfiles, requirements.txt, .gitignore and their role in deployment. We will then deploy a test dashboard online using Heroku.