Loading repository data…
quantumudit / repository
This project automates the Python ETL process through GitHub Actions. The updated data is then analyzed and visualized with Jupyter Notebook and Power BI, respectively, to generate powerful insights
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
This project focuses on getting the daily India statewide COVID-19 data from Ministry of Health & Family Welfare, performing exploratory data analysis to generate insights and visualize them with the help of Power BI.
The repository directory structure is as follows:
├───Daily-COVID19-India-Statewide-Analysis │ ├───01_ETL │ ├───02_DATA │ └───05_RESOURCES
The type of content present in the directories is as follows:
01_ETL
This directory contains the ETL script that takes the JSON response from the target website as input, transforms it and exports an analysis-ready dataset into the 02_DATA directory.
02_DATA
This directory contains the data that can be directly used for exploratory data analysis and data visualization purposes.
03_ANALYSIS
This directory contains the python notebooks that analyzes the clean dataset to generate insights
04_DASHBOARD
This directory contains the python notebook with an embedded Power BI report that visualizes the data. The Power BI dashboard contains slicers, cross-filtering and other advance capabilities that end user can play with to visualize a specific facet of the data or, to get additional insights.
05_RESOURCES
This directory contains images, icons, layouts, etc. that are used in this project
The major skills that are required as prerequisite to fully understand this project are as follows:
In order to complete the project, I've used the following applications and libraries
The choice of applications & their installation might vary based on individual preferences & system settings.
The project architecture is quite straight forward and can be explained through the below image:
As per the above workflow; we are first requesting the JSON data from the website which is then processed and cleaned with another ETL specific Python script.
Finally; we leverage the clean & analysis-ready dataset for some exploratory data analysis (EDA) using Jupyter Notebook and creating an insightful report using Power BI
Selected from shared topics, language and repository description—not editorial ratings.
lakshmi-aluru-dev /
End-to-End Data Analytics Capstone Project This project is a comprehensive, end-to-end data analytics pipeline that demonstrates proficiency in a wide range of tools and methodologies. It covers the full lifecycle of a data project, from initial planning and data extraction to automated deployment and final visualization.
38/100 healthThe ETL job has been also scheduled to run everyday with the help of GitHub Actions so that, we can get updated data on daily basis.
To be updated.
If you have any doubts, queries or, suggestions then, please connect with me in any of the following platforms:
If you like my work then, you may support me at Patreon:
This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.
