Professional Summary
Results-driven Data Analyst with a strong foundation in Business Analytics. Expert in leveraging Python, SQL, and Power BI to transform complex datasets into actionable business intelligence. Proven track record in data cleaning, exploratory analysis, and building interactive dashboards that support strategic decision-making.
Work Experience
Data Analyst Intern
Workers Welfare Fund- Cleaned, analyzed, and visualized large organizational datasets to generate actionable insights.
- Built high-impact dashboards that supported data-driven decision-making and significantly improved operational performance.
Data Analyst Intern
iSeeWaves Cyber Security- Collected and analyzed freelance marketplace data (Upwork, Fiverr) using Python to study cybersecurity service demand and pricing trends.
- Developed Power BI dashboards and market intelligence reports to identify high-demand opportunities for the firm.
Key Projects
Analyzed key demographic and academic factors influencing student performance through rigorous Exploratory Data Analysis (EDA) in Python.
Executed end-to-end data processing for student scores and retail sales, transforming raw datasets into clean formats for deep EDA.
Built dynamic dashboards with DAX and interactive visuals to track real-time sales performance across multiple regions.
Designed a comprehensive Power BI dashboard to track student performance, evaluating demographics to identify areas for academic intervention.
Developed an interactive sales dashboard in Excel using advanced Pivot Tables and Power Query to visualize Superstore performance metrics.
Applied advanced DAX calculations in Power BI to visualize complex market trends and competitive landscapes for strategic insights.
Integrated extensive business data to design robust KPIs, identify top-performing regions, and optimize data-driven sales strategies.
Built a classification system using Logistic Regression and Random Forest to predict audience sentiment and engagement patterns from social media data.
Final Year Project
Petrol Price Prediction System
View on GitHub- Developed a deep learning–based petrol price forecasting system using TensorFlow and LSTM (Long Short-Term Memory) to analyze time-series data.
- Integrated key economic indicators including Brent crude oil prices, USD/PKR exchange rate, and petroleum levy to improve prediction accuracy.
- Applied data preprocessing and normalization (MinMaxScaler) to ensure consistent model performance across features.
- Designed a multi-layer LSTM model with dropout to prevent overfitting and enhance generalization.
- Achieved strong predictive performance with MAE: Rs. 4.25 and RMSE: Rs. 5.80, ensuring reliable forecasting.