This advanced, project-based course is designed to empower learners with the skills to apply, analyze, and transform MongoDB data using the PyMongo library in Python. Starting from fundamental data handling operations and culminating in powerful aggregation techniques, the course offers a structured and practical pathway for working with real-world document-oriented databases.
PyMongo - Advanced
PyMongo - Advanced
This course is part of Python NoSQL Data Engineering with MongoDB Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
July 2025
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 module introduces learners to the foundational aspects of working with MongoDB using PyMongo, the official Python driver for MongoDB. The module begins by setting the context of the course through its objectives, targeted skills, and conceptual groundwork. Learners are then guided through preparing and loading structured sample data into a MongoDB collection, followed by performing basic yet essential operations such as sorting and verifying the integrity of stored records. Through a project-driven approach, this module ensures that learners gain hands-on experience with PyMongo, equipping them to handle real-world document-oriented data tasks with confidence.
What's included
8 videos3 assignments
This module delves into the advanced operations of PyMongo, empowering learners to work with MongoDB queries at a deeper level. Through hands-on exercises and real-world scenarios, learners will explore the use of cursors, result manipulation with limit and skip, counting operations, and the power of indexing for performance optimization. The module then transitions into transforming and analyzing MongoDB data using aggregation pipelines and converting output to structured formats like pandas DataFrames. By the end of the module, learners will have a strong command of MongoDB’s programmatic querying and transformation capabilities using PyMongo.
What's included
9 videos3 assignments
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