PyMongo Case Study - Aggregating Customer Data of a Bank

About this course

Build practical PyMongo skills for banking data workflows by connecting Python with MongoDB and using aggregation pipelines to transform, segment, and analyze customer data. In this hands-on course, you’ll create a modular Python project, configure MongoDB connectivity with PyMongo, implement structured logging, and load customer data from CSV files. You’ll also validate datasets for completeness and consistency before preparing them for analysis. Next, you’ll design and execute multi-stage MongoDB aggregation pipelines using $match, $group, $project, and $sort. Through a realistic banking case study, you’ll filter and group records, transform raw customer data, organize segments according to business rules, and generate meaningful summaries for reporting and decision-making. This course is designed for learners seeking practical experience in PyMongo, MongoDB aggregation, and banking data analysis. Its end-to-end approach connects project setup, data ingestion, validation, segmentation, and analysis within one cohesive workflow. By the end, you’ll be able to build Python–MongoDB integrations, prepare banking datasets, construct aggregation pipelines, segment customer records, and analyze results to produce actionable insights. Enroll to develop applied database programming skills through a focused financial data project.