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Data Science:Covid-19 Data Analysis Visualization Deployment

Name: Data Science:Covid-19 Data Analysis Visualization Deployment
Rating: 4.5 (13 reviews)

A practical hands on data science Project on Covid-19 Data Analysis, Visualization(using Plotly Express) & Deployment

Created byAutomationGig .

Last updated 8/2025

English

English [Auto],

What you'll learn

Data Analysis and Understanding
Data Preparation
Performing Feature Engineering
Learn to create Plotly Bubble and Bar Charts
Folium to visualize the data on world map
Pushing your notebooks to GitHub repository
Deploying your project on Heroku Platform

Course content

6 sections • 21 lectures • 1h 57m total length

Project Overview1:25
Analyze a real worldwide covid-19 data set updated daily, visualize with bubble charts, line graphs, and world map using Jupyter notebooks, then deploy via a local server and Hiroku platform.
Installing Packages2:27
Discover how to prepare your covid-19 data analysis project by creating a requirements file, listing needed packages, and installing them all with a single pip command.

Importing Libraries6:23
Set up a Jupyter notebook for covid-19 data analysis by creating a folder, naming a project file, and importing libraries with clear comments for interactive visualization.
Loading the data from source4:12
Load data from a source in a jupiter notebook by importing from an online server or updated files daily, using read_csv for confirmed, deaths, recovered, and country data.
Understanding the Data6:47
Explore how COVID-19 data is loaded and inspected, examining province and country fields, coordinates, dates, and metrics like confirmed, deaths, recovered, active counts, and rates across multiple data frames.
Preparing the Data6:00
Convert all column names to lowercase and rename columns with slash or special characters to simpler names using Python's map function, preparing the data for downstream analysis and visualization.
Feature Engineering4:42
Apply feature engineering to covid-19 data by deriving new cases as the difference between consecutive days, using domain knowledge to extract meaningful features from raw data.
Extracting the Overall Worldwide Status Counts5:07
Compute worldwide totals by summing confirmed, deaths, recovered, and active cases across countries, then display the results in HTML within the notebook using Python and display tools.

Data Visualization using Plotly Bubble Charts14:52
Visualize confirmed cases, deaths, and recoveries with Plotly bubble charts, aggregating data by country totals. Use interactivity to adjust top countries and transition to line charts for trends.
Data Visualization using Plotly Line Charts8:39
Visualize covid-19 trends over time with Plotly line charts, presenting country-wide confirmed, deaths, recovered, and active cases, with interactive visuals for top countries.
Interactive Table Chart6:49
Build an interactive table chart with python dataframes to display covid-19 metrics, sorted by confirmed cases, with columns country, last update, confirmed, deaths, recovered, active, incidence rate, and mortality rate.
Data Visualization using Plotly Bar Charts9:48
Visualize the top countries by confirmed, dead, recovered, and active COVID-19 cases using bar charts and plotlines from the country data frame.
Data Visualization on World Map13:24
visualize covid-19 data on a world map by plotting latitude and longitude, handling missing values, and comparing volume libraries and plotly methods with interactive tooltips and log-scaled point sizes.

Preparing notebook for COVID19 Dashboard5:36
Learn how to prepare a covid-19 dashboard by duplicating the notebook, pruning unnecessary outputs, retaining essential data loading, cleaning, feature engineering, and visualizing with world map and bubble charts.
What is Voila and Installation Steps2:17
Install Voila and run your notebooks on a local server to turn Jupyter notebooks into interactive dashboards, enabling easy sharing and deployment as standalone web apps.
Running the Notebook on Local Voila Server3:27
Run the covid-19 dashboard notebook on a local voila server to launch visualizations as a standalone web app, and prepare for deployment on Heroku.

Updating your Project Directory3:12
Organize the project directory by keeping notebooks, a gitignore, a proc file, requirements, and a readme. Explain each file's role and prepare for pushing to GitHub and Heroku deployment.
Pushing your code to Github Repository5:10
Push your code to a GitHub repository by creating a covid-19-dashboard repo, initialize and commit locally, set remote origin, and push to master, then prepare for deployment on Heroku.
Project Deployment on Heroku Platform7:23
Deploy a covid-19 dashboard to the heroku platform by connecting github, selecting a branch, and choosing manual deployments, enabling worldwide access and live visualizations.

Requirements

Very Basic knowledge of Python
Basic knowledge on Git

Description

This course is about Data Analysis, Visualization on COVID19 data (from authentic and reputed source which is updated daily) & Deployment. This course will teach you everything you need to know from Data Analysis Visualization to deploying it on Cloud platforms.

This course will walk you through the initial data analysis and understanding, data preparation and feature engineering techniques. We will use Plotly which helps us in creating beautiful graphs, hence we will be learning to use Plotly for data visualization mainly we will use Bubble Charts, Bar Charts and Line Plots.

We will also learn techniques to create an interactive feature which will help us to interact with our graphs, changing value at runtime, this will help the users to better interact and play around with the data real time.

We will learn to plot the COVID19 data on world map.

At the end we will learn to deploy the same on Cloud.

Please note that this is a hands on course that means I will code and then you will code.

I have split and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1 : Importing Libraries

Task 2 : Loading the data from source (the data source is updated every 24 hours)

Task 3 : Data Understanding

Task 4 : Data Preparation

Task 5 : Extracting the overall worldwide status counts.

Task 6 : Performing Feature Engineering.

Task 7 : Aggregating and visualizing the top n countries having highest new confirmed cases using Plotly Bubble Charts.

Task 8 : Aggregating and visualizing the top n countries having highest new death cases using Plotly Bubble Charts.

Task 9 : Aggregating and visualizing the top n countries having highest new recovered cases using Plotly Bubble Charts.

Task 10 : Visualizing the trend of confirmed cases over time using Plotly Line Charts.(Option to select country from drop down)

Task 11 : Visualizing the trend of death cases over time using Plotly Line Charts.(Option to select country from drop down)

Task 12 : Visualizing the trend of recovered cases over time using Plotly Line Charts.(Option to select country from drop down)

Task 13 : Table chart to showcase the top n countries (where n can be changed at chart level) sorted by confirmed cases in Descending order.

Task 14 : Table chart to showcase the Confirmed/Death/Recovered/Active cases for a country (where the country name can be changed at UI level).

Task 15 : Visualizing the top n worst hit countries (where n can be changed at chart level) with respect to confirmed cases in descending order using Plotly Bar Charts.

Task 16 : Visualizing the top n worst hit countries (where n can be changed at chart level) with respect to death cases in descending order using Plotly Bar Charts.

Task 17 : Visualizing the top n worst hit countries (where n can be changed at chart level) with respect to active cases in descending order using Plotly Bar Charts.

Task 18 : Visualizing the top n countries (where n can be changed at chart level) with respect to recovered cases in descending order using Plotly Bar Charts.

Task 19 : Visualizing the global spread of COVID19 on world map.

Task 20 : Preparing a separate notebook for COVID19 Dashboard containing only the data analysis and visualization graphs.

Task 21 : What is Voila and Installation steps.

Task 22 : How to run your notebook on Voila Server in your local machine.

Task 23 : Pushing your project to GitHub repository.

Task 23 : Hosting your project on Heroku Platform for free.

Data Analysis and Visualization is the most demanded skill of the 21st century and this skill can be yours just for the price of lunch.

You will receive :

1. Certificate of completion from AutomationGig.

2. The Jupyter notebook are provided at the end of the course in the resource section.

3. All other supporting project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!

[Please note that this course and its related contents are for educational purpose only]

Happy Learning !!

Who this course is for:

Students and professionals who want to learn Data Analysis and Visualization
Students and professionals who is interested in learning Plotly
Anyone who is interested in visualizaing real and authentic COVID19 data from reputed source
Professionals who knows data analysis but wants to deploy their data analysis visualizations on cloud

Data Science:Covid-19 Data Analysis Visualization Deployment

What you'll learn

Explore related topics

Course content

Introduction and Getting Started2 lectures • 4min

Data Understanding, Preparation and Feature Engineering6 lectures • 33min

Data Visualization5 lectures • 54min

Running the Notebook on Local Server3 lectures • 11min

Deploying your project on Heroku Platform3 lectures • 16min

Project Files and Code2 lectures • 1min

Requirements

Description

Who this course is for: