Profile
Junior Data Analytics with a University of Tehran Data Science background and an academic foundation in Electrical Engineering (Telecommunications & ICT). Experienced in SQL/databases, BI, statistics, Python, and machine learning, with hands-on work across tabular and image data. Passionate about building intelligent dashboards and analytical solutions that convert raw data into clear, actionable insights for business decision-making.

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Skills
Databases : SQL Server 

Business Intelligence & Dashboarding : Power BI, Excel 

Programming & Data Analysis : Python 

Statistical Analysis : IBM SPSS Modeler

Data Mining & Workflow Automation : KNIME

Storytelling with data : Tableau

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projects 
RFM Analysis and Customer Segmentation                                                                     

Performed customer segmentation by calculating Recency, Frequency, and Monetary (RFM) metrics to identify customer loyalty patterns and high-value segments. The analysis was designed to support targeted marketing initiatives, improve marketing return on investment, and enable proactive churn-reduction strategies.

Sales Growth & Profitability Root-Cause Analysis Dashboard                                         

Performed customer segmentation by calculating Recency, Frequency, and Monetary (RFM) metrics to identify customer loyalty patterns and high-value segments. The analysis was designed to support targeted marketing initiatives, improve marketing return on investment, and enable proactive churn-reduction strategies.

Advertising Data Analysis Using Python, Statistical, Matrix & Bayesian Methods          

Analyzed advertising data in Python to examine dataset structure and relationships among key variables. Applied linear algebra techniques for matrix-based analysis and dimensionality reduction, fitted a linear regression model using ordinary least squares, and used Bayesian inference to estimate the probability of achieving high sales outcomes.

Inventory Retention & Stock Depletion Analysis

Calculated inventory retention indicators and estimated stock depletion time based on average sales velocity. The analysis was developed to help inventory managers identify optimal replenishment windows, reduce stockout risk, and improve inventory planning and product availability.

Breast Cancer Tumor Classification Using IBM SPSS Modeler

Designed, implemented, and evaluated machine learning models for breast tumor detection and classification using IBM SPSS Modeler. The project covered data preprocessing, optimal feature selection, model training, and comparative performance evaluation, demonstrating the potential of artificial intelligence as a clinical decision-support tool alongside medical specialists.