MongoDB Fundamentals

1. Basics of MongoDB

To better understand how MongoDB works, we will start with a brief overview of relational databases and their structure. Then you'll learn what document-oriented stores are, how they differ from SQL databases, and how a document is structured in MongoDB (including nested subdocuments).

2. Development Environment and Dataset

You will set up a test environment using Docker and connect a user-friendly interface (Mongo Express) to view data and manage collections. We will use Mongo and Mongo-Express images from Docker Hub and create a Docker Compose file. I will also introduce you to the dataset that you will be working with during the course.

3. Designing a MongoDB Schema

You will design a schema for your data, learn how to perform queries, and manually create indexes. This will give you an understanding of how to properly organize the structure of collections and why the chosen approach is important for performance.

4. Working with MongoDB

Before practicing, we will discuss basic CLI commands for working with a database, collections, and documents. I will show you how to install Python and PyMongo using WSL (Windows Subsystem for Linux).

In the practical section, you will learn to use key CRUD operations: create, read, update, delete documents in MongoDB. You will also work with arrays of subdocuments, learning how to create, modify, and query them. In the end, we will consider the main operators and get acquainted with transactions in MongoDB.

5. MongoDB in Data Science Platforms

To conclude the course, you will see how MongoDB can be used in data analysis projects. We will explore a typical case and discuss in which tasks document-oriented databases are truly beneficial.