Data Science Internship at Talan

Project Overview

During my internship at Talan, I worked on comprehensive banking data analysis solutions using cutting-edge cloud technologies. The project focused on building scalable data pipelines, processing large volumes of banking transactions, and implementing machine learning models for predictive analytics.

Key Responsibilities

• Designed and implemented ETL pipelines using Azure Data Factory and Databricks
• Processed and analyzed banking data using Apache Spark for distributed computing
• Developed machine learning models for customer segmentation and predictive clustering
• Created interactive dashboards for business intelligence and real-time monitoring
• Optimized data workflows reducing processing time by 40%
• Collaborated with cross-functional teams to deliver data-driven insights

Technical Implementation

The architecture leveraged Microsoft Azure's ecosystem, with Databricks serving as the unified analytics platform. We implemented Delta Lake for reliable data lakes, used MLflow for experiment tracking, and deployed models using Azure ML Services.

Results & Impact

The project successfully automated data processing workflows, enabling real-time analytics and reducing manual intervention by 70%. The predictive models achieved 85% accuracy in customer behavior prediction, helping the business make data-driven decisions.