Data Cleaning
in SQL
In this project we take raw housing data and transform
it in SQL Server to make it more usable for analysis.
A highly skilled Data Analyst with expertise in SQL, Python, and Power BI, excelling at transforming raw data into actionable insights. With hands-on experience in building predictive models and analyzing complex datasets, poised to drive data-driven decision-making and optimize business outcomes.
In this project we take raw housing data and transform
it in SQL Server to make it more usable for analysis.
This SQL project delves into real-world global COVID-19 data, focusing on data exploration techniques. Through SQL queries and analysis, it seeks to uncover insights and patterns within the pandemic's impact, aiding in understanding and decision-making.
This project extends COVID-19 data exploration conducted in SQL to trend analysis using Tableau, offering a comprehensive view of pandemic patterns and shifts. By combining SQL's data exploration capabilities with Tableau's visualization tools, it provides actionable insights for understanding and responding to COVID-19 trends.
Utilized Power BI to compare product performance across different regions and assess sales performance by salesperson. The analysis provided key insights into regional sales trends and identified top-performing salespeople, enabling more informed decision-making and targeted strategies for improving overall sales effectiveness.
Through meticulous data understanding, variable transformation, and linear regression application, evaluate model performance to provide actionable insights, supported by Excel datasets for both training and testing phases.
This project focuses on utilizing Excel Dashboards to analyze trends of Bank Loan Application. By visualizing loan application data, insightful trends and patterns are uncovered, aiding in strategic decision-making and risk performance evaluation within the Finance domain.
Utilizing Power BI, Compare user metrics such as subscriber growth, viewing habits, and platform preferences across Netflix and Amazon Prime.
Analyze and contrast the movie libraries, including genre distribution, popular titles, and content availability on both streaming platforms.