This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework.
You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data. This will lay the groundwork for the remainder of the course where you'll dive deep and learn about schema design, relational data migrations, and machine learning with
MongoDB.
By the end of this course you'll understand how to best use MongoDB and its Aggregation Framework in your own data science workflow.
Overview
Syllabus
- The Fundamentals of MongoDB Aggregation
- In this module you'll learn the fundamentals of MongoDB's Aggregation Framework. This will cover basics like filtering and sorting, as well as how to transform array data, how to group documents together, how to join data, and how to traverse graph data.
- Leveraging MongoDB's Flexible Schema
- This module is going to be focused on the different ways you can leverage MongoDB's flexible schema. You'll learn how to migrate a relational schema, how to enhance existing schemas, and how to merge datasets via an entity resolution technique.
- Machine Learning with MongoDB
- This module is focused on demonstrating how MongoDB can be used in different machine learning workflows. You'll learn how to perform machine learning directly in MongoDB, how to prepare data for machine learning with MongoDB, and how to analyze data with MongoDB in preparation of doing machine learning in Python.
Taught by
Nathan Leniz and Kirby Kohlmorgen
