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PyMongo Case Study - Aggregating Customer Data of a Bank

EDUCBA via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 weeks, 3 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English, Kazakh, Arabic, German, Korean, Japanese, Italian, Indonesian, French, Spanish

Found in

Part of

Python NoSQL Data Engineering with MongoDB

Overview

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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.

Syllabus

Building the MongoDB-Python Integration
- This module introduces learners to the foundational components of the PyMongo-based banking data project. It begins with project setup, including environment preparation, structured logging, and modular programming practices. Learners will then connect to MongoDB using PyMongo, fetch and load CSV-based customer data, and apply essential data validation techniques. The module emphasizes practical integration between Python and MongoDB to facilitate structured data ingestion and preparation for aggregation.
Data Aggregation and Customer Segmentation
- This module focuses on using MongoDB's powerful aggregation framework to segment and analyze customer data. Learners will begin by defining and structuring aggregation pipelines to filter, group, and transform raw banking data. They will then segment this data into logical collections based on business rules and perform advanced operations using stages such as $match, $group, $project, and $sort. By the end of this module, learners will have practical experience in converting unstructured datasets into meaningful summaries for decision-making and reporting.