PyMongo Case Study - Aggregating Customer Data of a Bank

This course is part of Python NoSQL Data Engineering with MongoDB Specialization

Instructor: EDUCBA

Access provided by Dnipro University of Technology

2 modules

Gain insight into a topic and learn the fundamentals.

4.7

(22 reviews)

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

4.7

(22 reviews)

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments

Taught in English

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This course is part of the Python NoSQL Data Engineering with MongoDB Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 2 modules in this course

This practical, hands-on course empowers learners to apply the PyMongo library in building robust data solutions tailored to banking environments. Through a structured case study, participants will construct a modular Python-MongoDB integration that loads, segments, and analyzes real-world customer data.

In Module 1, learners will set up the project environment, connect to MongoDB using PyMongo, and prepare structured logging and file ingestion mechanisms. In Module 2, they will design advanced aggregation pipelines to segment customer records based on banking logic and generate insights through data grouping, transformation, and analysis. By the end of the course, learners will have the skills to implement end-to-end data workflows in MongoDB using PyMongo—ranging from raw file loading to actionable data summarization—preparing them for real-world data engineering and analytics tasks in the financial domain.

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.

What's included

8 videos3 assignments

8 videosTotal 64 minutes

Introduction to Project7 minutes
Mongo Connection4 minutes
Creating The Project part 18 minutes
Creating The Project part 210 minutes
Creating The Project part 35 minutes
Fetching Mongo Collection12 minutes
File Load to Mongo9 minutes
File Load to Mongo Continue7 minutes

3 assignmentsTotal 50 minutes

Graded - Building the MongoDB-Python Integration30 minutes
Project Setup and MongoDB Connection10 minutes
Working with MongoDB Data Sources10 minutes

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.