I help service-based businesses recover from damaging online reviews by implementing data-driven reputation systems. I audit online profiles to identify critical patterns, diagnose root causes of negative feedback, and execute strategies that transform public complaints into trust signals. Alongside this, I use Python, SQL, and automation tools to analyze and enrich datasets, uncover trends, and support business growth—turning raw data into actionable insights that improve revenue and customer acquisition.

Worked on NGO data analysis under the Amdad project. Collected and cleaned large datasets, stored data in PostgreSQL, and created reports to highlight key insights. Collaborated with teams to refine data collection methods and ensure data accuracy.

Developed multiple end-to-end data analysis projects showcasing skills in data cleaning, visualization, and business insight generation. Worked with real-world and API-based datasets covering retail, sales, and e-commerce domains. Created interactive Power BI dashboards and detailed reports to demonstrate analytical thinking and storytelling with data.

Completed a comprehensive 12-course program covering Python, SQL, Data Analysis, Data Visualization, and Machine Learning. Gained hands-on experience in applying data science techniques to real-world business problems.

Completed a government-supported training focused on applying AI and Data Science to solve practical business and real-world problems. Gained hands-on experience with Python, data preprocessing, model development, and performance evaluation. Learned key concepts of machine learning, artificial intelligence, and data visualization to extract insights and make data-driven decisions.

Learned to clean, organize, and analyze business data using Excel, Power Query, and Power Pivot. Gained practical experience in building interactive dashboards and reports in Power BI to visualize KPIs, track performance, and support data-driven decision-making.