Data Analyst

Technical Skills: Python, SQL, R, Tableau, MS Power BI

Education

B.S, Software Engineering NUST Islamabad

Certifications

Google Advance Data Analytics Professional Certificate
Google Data Anlytics Professional Certificate
Google Business Intelligence Specialization
IBM Data Analyst Professional Certificate

Badges

Work Experience

Data Analyst@ S&P Global (June 2022 - Present)

Implemented data validation protocols, reducing data anomalies by 60%.
Optimized SQL queries, improving data retrieval efficiency by 40%.
Developed predictive models using Python, resulting in a 15% increase in forecast accuracy.
Collaborated on A/B testing for pricing strategies, yielding a 12% revenue boost.

Projects

Waze Churn Analysis Publication

Created a ML model to forecast user attrition within the Waze application. Our approach involves five key stages: data inspection and analysis, exploratory data analysis (EDA), hypothesis testing, regression modeling, and the development of a tailored ML model. Operating under Google’s umbrella, Waze was provided with valuable insights and predictions that helped user churn effectively, ultimately enhancing the user experience of this GPS navigation service.

Mercedes-Benz HR Analytics - Employee Retention and Attrition Prediction Publication

Led the analysis of HR data at Mercedes-Benz to identify factors influencing employee attrition and developed a predictive Machine Learning model for attrition forecasting. This project aimed to enhance employee satisfaction and reduce turnover by leveraging data-driven insights. Key tasks included data cleaning, regression modeling, and tree-based modeling (Decision Tree & Random Forest), resulting in actionable recommendations for HR strategies.

Stack Overflow Case Study Publication

Conducted an in-depth exploratory data analysis (EDA) using python on the Stack Overflow Annual Developer Survey 2020 dataset to uncover valuable insights. This project involved key activities such as dataset selection and retrieval, thorough data cleaning and preparation, exploratory analysis through data visualization, and the formulation and exploration of intriguing questions within the dataset. The results were summarized, and meaningful conclusions were drawn from the analysis, demonstrating proficiency in data analysis and visualization techniques.