COVID 19
DATA EXPLORATION
In this project we use SQL Server to explore global COVID 19 data. Performed data cleaning and data exploration using SQL, created insights for visualization.
COVID 19
CYCLISTIC BIKE SHARE COMPANY DATA ANALYSIS
I utilized SQL and Python to address the imbalanced dataset and performed data cleaning. By leveraging information on rides per month, per year, and per day, I generated visualizations using Tableau. This aided the organization in enhancing sales and encouraged the conversion of regular members into casual members.
Data Professional
Survey Breakdown
I have conducted data analysis and visualization, creating dashboards using tools such as Excel, Power Query, and Power BI. I delved into data roles to uncover trends, visualizing information such as average salary, data roles per county, total survey results, and happiness with work/life and salary. Tool Used:: Excel, Power Query, Power B
DATA VISUALIZATION
ANALYSIS FOR AIRBNB
Using Excel, Tableau, and Kaggle datasets, I conducted data visualization for Airbnb. Analysis included peak business times, zipcode-based pricing trends, yearly revenue, average bedroom prices, and bedroom listings count. Visual representations revealed peak periods, pricing strategies, revenue trends, and market insights. This project informs strategic decisions, enhancing Airbnb's market standing.
Organizational Lead Performance
Data Analysis Report
Performing a thorough analysis of associates ABC, KLM, and XYZ, I utilized Excel and SQL to derive essential KPIs like Average Leads Generated/day. Power BI aided in crafting impactful line charts illustrating performance trends. Synthesizing data-driven recommendations, a concise two-page PDF report was created, meeting company specifications. The report not only highlighted significant performance trends but also offered actionable suggestions for enhancing associate performance
Global Insights: COVID-19 Data Analysis and Visualization
Engaged in a comprehensive COVID-19 analysis using Python, Pandas, and NumPy in Jupyter Notebook. Rigorously cleaned data, crafted impactful visualizations with Matplotlib, and derived key KPIs. Uncovered insights, including global trends, CFR, Recovery Rate, and identified regions with the highest active cases percentage and average metrics.