COVID-19 Dashboard

Table of Content

• Introduction
• Goals
• Data Cleaning
• Database
• API
• Dashboard
• How To Use It
• Credits

Introduction

This is my Bootcamp Mid Project where I created a dashboard about COVID-19 in Germany and Venezuela. It is my first real project since I jumped into data analytics field. You are going to find how I did it and the goals that was setted at then end of it. I hope you like it 🤟

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Goals

L1

• Create Api in FastApi
• Create Dashboard in streamlit
• Database in MongoDB or PostgreSWL

L2

• Use of geospatial data and geoqueries in MongoDB or Postgres (Using PostGIS)
• Have the database in the Cloud (There are free services in MongoDB Atlas, Heroku Postgres, dentre others)
• Generate pdf report of the data visible in Streamlit, downloadable via button.
• A multi-page dashboard in Streamlit.

L3

• Have the dashboard send you the pdf report by e-mail
• To be able to upload new data to the database via API (username and password as request headers)

L4

• To be able to update the database via Streamlit (with username and password, in a separate page. The dashboard must make the previous request that adds data via API)
• Create Docker container and deploy the services in the cloud (Heroku. The two services must be uploaded separately)

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Data Cleaning

For this project I used this dataset from Kaggle Time Series Data Covid-19 Global. You can find the csv files within data folder: confirmed_global.csv, deaths_global.csv and recovered_global.csv.

I did some data cleaning to get only cases form Germany and I dropped the Province/State column from the dataset because it was empty, at least for this country. Then, I created three new files as the original dataset but named different: germany_confirmed_cases.csv, germany_confirmed_deaths.csv and germany_confirmed_recovered.csv.

If you want yo explore how I did the data cleaning, check the file within data folder or clicking here.

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Database

The datasets cleaned was uploaded to Mongo DB as a CSV file for being processed through the API.

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API

I created an API with its routers to extract data when it is neccesary.

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Dashboard

The dashboard was created using Streamlit interface.

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How To Use It

• Clone repository

• Run API

  uvicorn main:app

• Run Streamlit

  streamlit run main.py

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Credits

I'd like to thanks to my teachers Santino Lede and Daniel Alvarado for support me in each step of this project. Also, I want to thanks Nicolas Manduley repository, because it helped me to chart my data.

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Hi 👋 I'm Jesús Adraz!

Junior Data Engineer

I am a Junior Data Engineer passionate about analyzing data, creating dashboards and data driven cultures.

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