M121: The MongoDB Aggregation Framework (MongoDB)

Offered by MongoDB University,
M121: The MongoDB Aggregation Framework (MongoDB)

Learn how to use MongoDB's Aggregation Framework. This course will provide you with the knowledge to use MongoDB Aggregation Framework with confidence on your application development practices. You’ll build an understanding about how to use MongoDB Aggregation Framework pipeline, document transformation and data analysis. We will look into the internals of the Aggregation Framework alongside with optimization and pipeline building practices.

Prerequisites:
We highly recommend taking M001 or M220P or 3-6 months experience using MongoDB in application development.

Course Details
Total Estimated Effort

  • 8.5 Hours
  • Duration: 8 Chapters
  • Each chapter requires approximately 61 minutes to complete
  • Up to 60 days to complete from day of registration

Agenda

  • Chapter 0: Introduction and Aggregation Concepts
  • Chapter 1: Basic Aggregation - $match and $project
  • Chapter 2: Basic Aggregation - Utility Stages
  • Chapter 3: Core Aggregation - Combining Information
  • Chapter 4: Core Aggregation - Multidimensional Grouping
  • Chapter 5: Miscellaneous Aggregation
  • Chapter 6: Aggregation Performance and Pipeline Optimization
  • Final Exam

System Requirements
You will need access to a computer with Operating System: Mac OS X 10.7+ 64-bit, Ubuntu 14.04+ 64-bit, or Windows 8+ (64-bit) Web Browser: Firefox 39.0+ or Chrome 43+ (Internet Explorer is currently not supported)

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

M103: Basic Cluster Administration (MongoDB) MongoDB
MongoDB University

M103: Basic Cluster Administration (MongoDB)

Learn the essentials of database administration in MongoDB. This course will provide you with the tools and insight to administer basic MongoDB deployments with confidence. You'll build standalone nodes, replica sets and sharded clusters from scratch. These will serve as platforms to learn how administration varies depending on the makeup of a cluster.

Self Paced
Self-Paced
M220J: MongoDB for Java Developers (MongoDB) MongoDB
MongoDB University

M220J: MongoDB for Java Developers (MongoDB)

Learn the essentials of Java application development with MongoDB. This course will teach you how to use MongoDB as the database for a Java application. You will play the role of a back-end developer for a Java application, where your job is to implement the application's communication with MongoDB. Using the Java driver you will read and write data to the database, use the aggregation framework, manage the configuration of the database client, and create a robust application by handling exceptions and timeouts.

Self Paced
Self-Paced
Computing for Data Analysis (edX) EdX
Georgia Institute of Technology,GTx

Computing for Data Analysis (edX)

A hands-on introduction to basic programming principles and practice relevant to modern data analysis, data mining, and machine learning. The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data. In the course, you’ll see how computing and mathematics come together.

Aug 24th 2026
13-24 Weeks
Interprofessional Healthcare Informatics (Coursera) Coursera
University of Minnesota

Interprofessional Healthcare Informatics (Coursera)

Interprofessional Healthcare Informatics is a graduate-level, hands-on interactive exploration of real informatics tools and techniques offered by the University of Minnesota and the University of Minnesota's National Center for Interprofessional Practice and Education. We will be incorporating technology-enabled educational innovations to bring the subject matter to life. Over the 10 modules, we will create a vital online learning community and a working healthcare informatics network.

Aug 17th 2026
5-12 Weeks
Matrix Methods (Coursera) Coursera
University of Minnesota

Matrix Methods (Coursera)

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction.

Aug 17th 2026
5-12 Weeks