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OmarAhmedWahby / repository
This project demonstrates a full-cycle e-commerce data analysis solution developed collaboratively using modern data technologies
This project presents a business intelligence dashboard for an E-Commerce platform, analyzing sales, customer behavior, and operational performance over the first 4+ months of 2025. The analysis was conducted using Power BI, focusing on modeling, segmentation, and uncovering KPIs to support business decisions.
The data was structured into a star schema for flexible analysis:
This schema enables multi-angle exploration by time, customer, and product performance.
Note: Open the
.pbixfile using Power BI Desktop to explore the interactive report and all calculations.
Selected from shared topics, language and repository description—not editorial ratings.
DataWithBaraa /
This repository contains a collection of SQL scripts demonstrating various analytical techniques, such as changes over time, cumulative, performance, data segmentation, part-to-whole analysis.
tushar2704 /
This repository showcases my skills and experience in the field of data analysis. Here, you will find a collection of projects and analyses that demonstrate my ability to extract insights and make data-driven decisions.
AryaKoureshi /
This project demonstrates various signal processing techniques, such as signal generation, window functions, filtering, downsampling, zero-padding, and the application of time-frequency analysis using the Short-Time Fourier Transform (STFT).
DIGVISHRAYALA /
This repository contains a Jupyter Notebook developed as part of the CA-2 project. It focuses on data analysis using Python with libraries like Pandas, NumPy, Matplotlib, and others. The project demonstrates data loading, preprocessing, exploration, and visualization techniques suitable for academic submissions or real-world data insights.
lord-shaz /
This is python based project demonstrate web scrapping and building a console application that provide data analysis to the user.
nirdesh17 /
This project demonstrates the application of machine learning techniques to predict house prices based on various features. By analyzing the dataset, preprocessing the data, and selecting an appropriate model, we were able to achieve a high level of accuracy in predicting house prices. The trained model can be further refined and deployed.