I developed an interactive analytical dashboard which was written in Python by leveraging Dash and Plotly.

The dashboard is user-friendly and since Dash is a Python framework built on top of React, a JavaScript library, it is easy to integrate into the existing EO dashboard.

My project targets on answering all of the challenge questions, by implementing the following functionalities:

_ Selecting date range

_ Setting y-axis limit

_ Selecting type of gas

_ On/Off toggle for selective features

_ Different options for color and line modes

Additionally, Plotly provides extra tools such as a zoom-in window and download png.

Since I am a data scientist, wrangling with data is part of my daily work. Visualization is the most enjoyable activity to understand data. I have been a fan of Plotly and that's why this challenge caught my attention.

There are 5 stages: understanding the requirements, looking for tools to solve the problems, designing the UI/UX, working on the backend, and integrating the front and the back.

I used Python to write the back-end with libraries such as Pandas and NumPy.

For the front-end, I used Dash and Plotly with libraries such as Dash Bootstrap and Dash Core Components.

Since front-end has never been my strong suit, I watched a lot of youtube videos to understand the layout design. I also have problems connecting to the database. However, I think the most important thing is to complete the project, and show what could be implemented later. Therefore, I created a synthetic dataset that would look similar to the real one and answer all of the remaining challenge questions.

I have used the data from Data hub and NOAA for research purposes. But since the main challenge of the problem is creating the analytical solution, I focus my attention on the dashboard and using synthetic data to speed up the progess.

Video demo

Github

Data hub

NOAA

#air_quality #dashboard #analytics #datascience #python

This project has been submitted for consideration during the Judging process.